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Publications

Selected publications

  1. Jointly learning word embeddings using a corpus and a knowledge base (Journal article - 2018)
  2. Using k-Way Co-Occurrences for Learning Word Embeddings (Conference Paper - 2018)
  3. A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations (Journal article - 2015)
  4. Gender-preserving Debiasing for Pre-trained Word Embeddings (Conference Paper - 2019)
  5. Think Globally, Embed Locally - Locally Linear Meta-embedding of Words. (Conference Paper - 2018)
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2024

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.

Abuzour, A. S., Wilson, S. A., Woodall, A. A., Mair, F. S., Clegg, A., Shantsila, E., . . . Walker, L. E. (2024). A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.. PloS one, 19(8), e0299770. doi:10.1371/journal.pone.0299770

DOI
10.1371/journal.pone.0299770
Journal article

In-Contextual Gender Bias Suppression for Large Language Models.

Oba, D., Kaneko, M., & Bollegala, D. (2024). In-Contextual Gender Bias Suppression for Large Language Models.. In Y. Graham, & M. Purver (Eds.), EACL (Findings) (pp. 1722-1742). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2024.findings-eacl/

Conference Paper

Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings

Zhang, G., Zhou, Y., & Bollegala, D. (2024). Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings. In 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 6530-6543).

Conference Paper

Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER

Abaho, M., Bollegala, D., Leeming, G., Joyce, D., & Buchan, I. (2024). Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 5013-5029). Association for Computational Linguistics. doi:10.18653/v1/2024.naacl-long.280

DOI
10.18653/v1/2024.naacl-long.280
Conference Paper

Preface by the Conference Organizers

Bollegala, D., Shwartz, V., & Camacho-Collados, J. (2024). Preface by the Conference Organizers. Proceedings of the Annual Meeting of the Association for Computational Linguistics, IV.

Journal article

Report on the 1st Symposium on NLP for Social: Good (NSG 2023)

Sen, P., Saha, T., & Bollegala, D. (2023). Report on the 1st Symposium on NLP for Social: Good (NSG 2023). ACM SIGIR Forum, 57(2), 1-9. doi:10.1145/3642979.3642989

DOI
10.1145/3642979.3642989
Journal article

Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures

Chen, J., He, X., Bollegala, D., & Miyao, Y. (2024). Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 3760-3772).

Conference Paper

2023

How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews?

Abuzour, A., Wilson, S., Woodall, A., Mair, F., Bollegala, D., Cant, H., . . . Walker, L. (2023). How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews?. American Academy of Family Physicians. doi:10.1370/afm.22.s1.4823

DOI
10.1370/afm.22.s1.4823
Report

Learning to Predict Concept Ordering for Common Sense Generation

Zhang, T., Bollegala, D., & Peng, B. (2023). Learning to Predict Concept Ordering for Common Sense Generation. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 10-19). Association for Computational Linguistics. doi:10.18653/v1/2023.ijcnlp-short.2

DOI
10.18653/v1/2023.ijcnlp-short.2
Conference Paper

The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated

Kaneko, M., Bollegala, D., & Okazaki, N. (2023). The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 29-36). Association for Computational Linguistics. doi:10.18653/v1/2023.ijcnlp-short.4

DOI
10.18653/v1/2023.ijcnlp-short.4
Conference Paper

A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings.

Bollegala, D., Otake, S., Machide, T., & Kawarabayashi, K. -I. (2023). A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings.. In J. C. Park, Y. Arase, B. Hu, W. Lu, D. Wijaya, A. Purwarianti, & A. A. Krisnadhi (Eds.), IJCNLP (Findings) (pp. 65-79). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2023.findings-ijcnlp/

Conference Paper

A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models

Zhou, Y., Camacho-Collados, J., & Bollegala, D. (2023). A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 11082-11100). Association for Computational Linguistics. doi:10.18653/v1/2023.emnlp-main.683

DOI
10.18653/v1/2023.emnlp-main.683
Conference Paper

A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models.

Zhou, Y., Camacho-Collados, J., & Bollegala, D. (2023). A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models.. In H. Bouamor, J. Pino, & K. Bali (Eds.), EMNLP (pp. 11082-11100). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2023.emnlp-main/

Conference Paper

A Word Sense Distribution-based approach for Semantic Change Prediction

Tang, X., Zhou, Y., Aida, T., Sen, P., & Bollegala, D. (2023). A Word Sense Distribution-based approach for Semantic Change Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 3575-3590). Association for Computational Linguistics. doi:10.18653/v1/2023.findings-emnlp.231

DOI
10.18653/v1/2023.findings-emnlp.231
Conference Paper

Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples

Kaneko, M., Bollegala, D., & Okazaki, N. (2023). Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 2857-2863). Association for Computational Linguistics. doi:10.18653/v1/2023.eacl-main.209

DOI
10.18653/v1/2023.eacl-main.209
Conference Paper

Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples.

Kaneko, M., Bollegala, D., & Okazaki, N. (2023). Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples.. In EACL (pp. 2849-2855).

Conference Paper

Evaluating the Robustness of Discrete Prompts

Ishibashi, Y., Bollegala, D., Sudoh, K., & Nakamura, S. (2023). Evaluating the Robustness of Discrete Prompts. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (pp. 2365-2376).

Conference Paper

Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders.

Cao, G., Jiang, J., Bollegala, D., & Luo, S. (2023). Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders.. In IROS (pp. 10800-10805).

Conference Paper

Learning to Predict Concept Ordering for Common Sense Generation.

Zhang, T., Bollegala, D., & Peng, B. (2023). Learning to Predict Concept Ordering for Common Sense Generation.. In J. C. Park, Y. Arase, B. Hu, W. Lu, D. Wijaya, A. Purwarianti, & A. A. Krisnadhi (Eds.), IJCNLP (2) (pp. 10-19). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2023.ijcnlp-short/

Conference Paper

Solving Cosine Similarity Underestimation between High Frequency Words by \ell₂ Norm Discounting.

Wannasuphoprasit, S., Zhou, Y., & Bollegala, D. (2023). Solving Cosine Similarity Underestimation between High Frequency Words by \ell₂ Norm Discounting.. In ACL (Findings) (pp. 8644-8652).

Conference Paper

Solving Cosine Similarity Underestimation between High Frequency Words by ℓ2 Norm Discounting Norm Discounting

Wannasuphoprasit, S., Zhou, Y., & Bollegala, D. (2023). Solving Cosine Similarity Underestimation between High Frequency Words by ℓ2 Norm Discounting Norm Discounting. In Findings of the Association for Computational Linguistics: ACL 2023 (pp. 8644-8652). Association for Computational Linguistics. doi:10.18653/v1/2023.findings-acl.550

DOI
10.18653/v1/2023.findings-acl.550
Conference Paper

Swap and Predict - Predicting the Semantic Changes in Words across Corpora by Context Swapping.

Aida, T., & Bollegala, D. (2023). Swap and Predict - Predicting the Semantic Changes in Words across Corpora by Context Swapping.. In H. Bouamor, J. Pino, & K. Bali (Eds.), EMNLP (Findings) (pp. 7753-7772). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2023.findings-emnlp/

Conference Paper

The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated.

Kaneko, M., Bollegala, D., & Okazaki, N. (2023). The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated.. In J. C. Park, Y. Arase, B. Hu, W. Lu, D. Wijaya, A. Purwarianti, & A. A. Krisnadhi (Eds.), IJCNLP (2) (pp. 29-36). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2023.ijcnlp-short/

Conference Paper

Together We Make Sense-Learning Meta-Sense Embeddings.

Luo, H., Zhou, Y., & Bollegala, D. (2023). Together We Make Sense-Learning Meta-Sense Embeddings.. In ACL (Findings) (pp. 2638-2651).

Conference Paper

Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings.

Aida, T., & Bollegala, D. (2023). Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings.. In ACL (Findings) (pp. 6868-6882).

Conference Paper

Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation.

Cao, G., Jiang, J., Mao, N., Bollegala, D., Li, M., & Luo, S. (2023). Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation.. In ICRA (pp. 12443-12449).

Conference Paper

2022

Random projections and kernelised leave one cluster out cross validation: universal baselines and evaluation tools for supervised machine learning of material properties

Durdy, S., Gaultois, M. W., Gusev, V. V., Bollegala, D., & Rosseinsky, M. J. (n.d.). Random projections and kernelised leave one cluster out cross validation: universal baselines and evaluation tools for supervised machine learning of material properties. Digital Discovery, 1(6), 763-778. doi:10.1039/d2dd00039c

DOI
10.1039/d2dd00039c
Journal article

Unmasking the Mask - Evaluating Social Biases in Masked Language Models

Kaneko, M., & Bollegala, D. (2022). Unmasking the Mask - Evaluating Social Biases in Masked Language Models. In THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE (pp. 11954-11962). Retrieved from https://www.webofscience.com/

Conference Paper

Assessment of contextualised representations in detecting outcome phrases in clinical trials

DOI
10.48550/arxiv.2203.03547
Preprint

<i>Learning to Borrow</i> - Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

Hakami, H., Hakami, M., Mandya, A., & Bollegala, D. (2022). <i>Learning to Borrow</i> - Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion. In NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (pp. 2887-2898). Retrieved from https://www.webofscience.com/

Conference Paper

A Survey on Word Meta-Embedding Learning

Bollegala, D., & O' Neill, J. (2022). A Survey on Word Meta-Embedding Learning. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 5402-5409). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2022/758

DOI
10.24963/ijcai.2022/758
Conference Paper

Debiasing Isn't Enough! - on the Effectiveness of Debiasing MLMs and Their Social Biases in Downstream Tasks.

Kaneko, M., Bollegala, D., & Okazaki, N. (2022). Debiasing Isn't Enough! - on the Effectiveness of Debiasing MLMs and Their Social Biases in Downstream Tasks.. In N. Calzolari, C. -R. Huang, H. Kim, J. Pustejovsky, L. Wanner, K. -S. Choi, . . . S. -H. Na (Eds.), COLING (pp. 1299-1310). International Committee on Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2022.coling-1/

Conference Paper

Gender Bias in Masked Language Models for Multiple Languages

Kaneko, M., Imankulova, A., Bollegala, D., & Okazaki, N. (2022). Gender Bias in Masked Language Models for Multiple Languages. In NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (pp. 2740-2750). Retrieved from https://www.webofscience.com/

Conference Paper

Gender Bias in Masked Language Models for Multiple Languages.

Kaneko, M., Imankulova, A., Bollegala, D., & Okazaki, N. (2022). Gender Bias in Masked Language Models for Multiple Languages.. In M. Carpuat, M. -C. D. Marneffe, & I. V. M. Ruíz (Eds.), NAACL-HLT (pp. 2740-2750). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2022.naacl-main/

Conference Paper

Gender Bias in Meta-Embeddings

Kaneko, M., Bollegala, D., & Okazaki, N. (2022). Gender Bias in Meta-Embeddings. In Findings of the Association for Computational Linguistics: EMNLP 2022 (pp. 3118-3133). Association for Computational Linguistics. doi:10.18653/v1/2022.findings-emnlp.227

DOI
10.18653/v1/2022.findings-emnlp.227
Conference Paper

Gender Bias in Meta-Embeddings.

Kaneko, M., Bollegala, D., & Okazaki, N. (2022). Gender Bias in Meta-Embeddings.. In EMNLP (Findings) (pp. 3118-3133).

Conference Paper

Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings.

Bollegala, D. (2022). Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings.. In L. D. Raedt (Ed.), IJCAI (pp. 4058-4064). ijcai.org. Retrieved from https://www.ijcai.org/proceedings/2022/

Conference Paper

Learning to Borrow- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion.

Hakami, H., Hakami, M., Mandya, A., & Bollegala, D. (2022). Learning to Borrow- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion.. In M. Carpuat, M. -C. D. Marneffe, & I. V. M. Ruíz (Eds.), NAACL-HLT (pp. 2887-2898). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2022.naacl-main/

Conference Paper

On the Curious Case of l2 norm of Sense Embeddings.

Zhou, Y., & Bollegala, D. (2022). On the Curious Case of l2 norm of Sense Embeddings.. In EMNLP (Findings) (pp. 2593-2602).

Conference Paper

Position-based Prompting for Health Outcome Generation

Abaho, M., Bollegala, D., Williamson, P. R., & Dodd, S. (2022). Position-based Prompting for Health Outcome Generation. In PROCEEDINGS OF THE 21ST WORKSHOP ON BIOMEDICAL LANGUAGE PROCESSING (BIONLP 2022) (pp. 26-36). Retrieved from https://www.webofscience.com/

Conference Paper

Position-based Prompting for Health Outcome Generation.

Abaho, M., Bollegala, D., Williamson, P., & Dodd, S. (2022). Position-based Prompting for Health Outcome Generation.. In D. Demner-Fushman, K. B. Cohen, S. Ananiadou, & J. Tsujii (Eds.), BioNLP@ACL (pp. 26-36). Association for Computational Linguistics. Retrieved from https://aclanthology.org/volumes/2022.bionlp-1/

Conference Paper

Query Obfuscation by Semantic Decomposition

Bollegala, D., Machide, T., & Kawarabayashi, K. -I. (2022). Query Obfuscation by Semantic Decomposition. In LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (pp. 6200-6211). Retrieved from https://www.webofscience.com/

Conference Paper

The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.

Walker, L. E., Abuzour, A. S., Bollegala, D., Clegg, A., Gabbay, M., Griffiths, A., . . . Buchan, I. (2022). The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.. Journal of multimorbidity and comorbidity, 12, 26335565221145493. doi:10.1177/26335565221145493

DOI
10.1177/26335565221145493
Journal article

Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models.

Takahashi, K., & Bollegala, D. (2022). Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models.. In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, . . . S. Piperidis (Eds.), LREC (pp. 7155-7163). European Language Resources Association. Retrieved from https://aclanthology.org/volumes/2022.lrec-1/

Conference Paper

Zero-shot Cross-Lingual Counterfactual Detection via Automatic Extraction and Prediction of Clue Phrases

Ushio, A., & Bollegala, D. (2022). Zero-shot Cross-Lingual Counterfactual Detection via Automatic Extraction and Prediction of Clue Phrases. In Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL) (pp. 28-37). Association for Computational Linguistics. doi:10.18653/v1/2022.mrl-1.3

DOI
10.18653/v1/2022.mrl-1.3
Conference Paper

2021

Detect and Classify - Joint Span Detection and Classification for Health Outcomes

Abaho, M., Bollegala, D., Williamson, P., & Dodd, S. (2021). Detect and Classify - Joint Span Detection and Classification for Health Outcomes. In 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021) (pp. 8709-8721). Retrieved from https://www.webofscience.com/

Conference Paper

<i>I Wish I Would Have Loved This One, But I Didn't</i> - A Multilingual Dataset for Counterfactual Detection in Product Reviews

O'Neill, J., Rozenshtein, P., Kiryo, R., Kubota, M., & Bollegala, D. (2021). <i>I Wish I Would Have Loved This One, But I Didn't</i> - A Multilingual Dataset for Counterfactual Detection in Product Reviews. In 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021) (pp. 7092-7108). Retrieved from https://www.webofscience.com/

Conference Paper

Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy

DOI
10.48550/arxiv.2110.02204
Preprint

Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance

DOI
10.48550/arxiv.2106.08007
Preprint

Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora

DOI
10.48550/arxiv.2105.04971
Preprint

Debiasing Pre-trained Contextualised Embeddings

Kaneko, M., & Bollegala, D. (2021). Debiasing Pre-trained Contextualised Embeddings. In 16TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2021) (pp. 1256-1266). Retrieved from https://www.webofscience.com/

Conference Paper

Dictionary-based Debiasing of Pre-trained Word Embeddings

Kaneko, M., & Bollegala, D. (2021). Dictionary-based Debiasing of Pre-trained Word Embeddings. In 16TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2021) (pp. 212-223). Retrieved from https://www.webofscience.com/

Conference Paper

RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding

Bollegala, D., Hakami, H., Yoshida, Y., & Kawarabayashi, K. -I. (2021). RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding. In 16TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2021) (pp. 1551-1565). Retrieved from https://www.webofscience.com/

Conference Paper

I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews

DOI
10.48550/arxiv.2104.06893
Preprint

Discrimination of human-written and human and machine written sentences using text consistency

Harada, A., Bollegala, D., & Chandrasiri, N. P. (2021). Discrimination of human-written and human and machine written sentences using text consistency. In 2021 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, AND INTELLIGENT SYSTEMS (ICCCIS) (pp. 41-47). doi:10.1109/ICCCIS51004.2021.9397237

DOI
10.1109/ICCCIS51004.2021.9397237
Conference Paper

Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy.

Bollegala, D., & Zhou, Y. (2021). Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy.. In K. Hu, J. -B. Kim, C. Zong, & E. Chersoni (Eds.), PACLIC (pp. 493-502). Association for Computational Lingustics. Retrieved from https://aclanthology.org/volumes/2021.paclic-1/

Conference Paper

2020

Multi-Source Attention for Unsupervised Domain Adaptation

Cui, X., & Bollegala, D. (2020). Multi-Source Attention for Unsupervised Domain Adaptation. In 1ST CONFERENCE OF THE ASIA-PACIFIC CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 10TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (AACL-IJCNLP 2020) (pp. 873-883). Retrieved from https://www.webofscience.com/

Conference Paper

DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Khemchandani, Y., O'Hagan, S., Samanta, S., Swainston, N., Roberts, T. J., Bollegala, D., & Kell, D. B. (2020). DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. Journal of Cheminformatics, 12(1). doi:10.1186/s13321-020-00454-3

DOI
10.1186/s13321-020-00454-3
Journal article

Meta-Embedding as Auxiliary Task Regularization.

O'Neill, J., & Bollegala, D. (2020). Meta-Embedding as Auxiliary Task Regularization.. In G. D. Giacomo, A. Catalá, B. Dilkina, M. Milano, S. Barro, A. Bugarín, & J. Lang (Eds.), ECAI Vol. 325 (pp. 2124-2131). IOS Press. Retrieved from https://doi.org/10.3233/FAIA325

Conference Paper

DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Khemchandani, Y., O'Hagan, S., Samanta, S., Swainston, N., Roberts, T., Bollegala, D., & Kell, D. (2020). DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. doi:10.21203/rs.3.rs-32446/v2

DOI
10.21203/rs.3.rs-32446/v2
Journal article

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Khemchandani, Y., O'Hagan, S., Samanta, S., Swainston, N., Roberts, T., Bollegala, D., & Kell, D. (2020). DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. doi:10.21203/rs.3.rs-32446/v1

DOI
10.21203/rs.3.rs-32446/v1
Journal article

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Khemchandani, Y., O’Hagan, S., Samanta, S., Swainston, N., Roberts, T., Bollegala, D., & Kell, D. (2020). DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach. doi:10.1101/2020.05.25.114165

DOI
10.1101/2020.05.25.114165
Journal article

Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction.

Bollegala, D., Kiryo, R., Tsujino, K., & Yukawa, H. (2020). Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction.. In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, . . . S. Piperidis (Eds.), LREC (pp. 3851-3860). European Language Resources Association. Retrieved from https://aclanthology.org/volumes/2020.lrec-1/

Conference Paper

Do not let the history haunt you -- Mitigating Compounding Errors in Conversational Question Answering

DOI
10.48550/arxiv.2005.05754
Preprint

Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents

DOI
10.48550/arxiv.2005.03066
Preprint

Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction

DOI
10.48550/arxiv.2002.11004
Preprint

Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering.

Mandya, A., O'Neill, J., Bollegala, D., & Coenen, F. (2020). Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering.. In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, . . . S. Piperidis (Eds.), LREC (pp. 2017-2025). European Language Resources Association. Retrieved from https://aclanthology.org/volumes/2020.lrec-1/

Conference Paper

Evaluating Co-reference Chains Based Conversation History in Conversational Question Answering

Mandya, A., Bollegala, D., & Coenen, F. (2020). Evaluating Co-reference Chains Based Conversation History in Conversational Question Answering. In Computational Linguistics (Vol. 1215, pp. 280-292). Springer Nature. doi:10.1007/978-981-15-6168-9_24

DOI
10.1007/978-981-15-6168-9_24
Chapter

Spatio-temporal Attention Model for Tactile Texture Recognition.

Cao, G., Zhou, Y., Bollegala, D., & Luo, S. (2020). Spatio-temporal Attention Model for Tactile Texture Recognition.. In IROS (pp. 9896-9902). IEEE. Retrieved from https://doi.org/10.1109/IROS45743.2020

Conference Paper

Tree-Structured Neural Topic Model

Isonuma, M., Mori, J., Bollegala, D., & Sakata, I. (2020). Tree-Structured Neural Topic Model. In 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020) (pp. 800-806). Retrieved from https://www.webofscience.com/

Conference Paper

2019

Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA

DOI
10.48550/arxiv.1911.12763
Preprint

Automated Bundle Pagination Using Machine Learning

Torrisi, A., Bevan, R., Atkinson, K., Bollegala, D., & Coenen, F. (2019). Automated Bundle Pagination Using Machine Learning. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law (pp. 244-248). ACM. doi:10.1145/3322640.3326726

DOI
10.1145/3322640.3326726
Conference Paper

"Touching to See" and "Seeing to Feel": Robotic Cross-modal SensoryData Generation for Visual-Tactile Perception

DOI
10.48550/arxiv.1902.06273
Preprint

"Touching to See" and "Seeing to Feel": Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception.

Lee, J. -T., Bollegala, D., & Luo, S. (2019). "Touching to See" and "Seeing to Feel": Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception.. In ICRA (pp. 4276-4282). IEEE. Retrieved from https://ieeexplore.ieee.org/xpl/conhome/8780387/proceeding

Conference Paper

A Dataset for Inter-Sentence Relation Extraction using Distant Supervision

Mandya, A., Bollegala, D., Coenen, F., & Atkinson, K. (2018). A Dataset for Inter-Sentence Relation Extraction using Distant Supervision. In PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018) (pp. 1559-1565). Retrieved from https://www.webofscience.com/

Conference Paper

Behavioural Biometric Continuous User Authentication Using Multivariate Keystroke Streams in the Spectral Domain

Alshehri, A., Coenen, F., & Bollegala, D. (2019). Behavioural Biometric Continuous User Authentication Using Multivariate Keystroke Streams in the Spectral Domain. In Communications in Computer and Information Science (pp. 43-66). Springer International Publishing. doi:10.1007/978-3-030-15640-4_3

DOI
10.1007/978-3-030-15640-4_3
Chapter

Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction.

Mandya, A., Bollegala, D., Coenen, F., & Atkinson, K. (2019). Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction.. In AKBC. Retrieved from https://openreview.net/group?id=AKBC.ws/2019/Conference

Conference Paper

Gender-preserving Debiasing for Pre-trained Word Embeddings

Kaneko, M., & Bollegala, D. (2019). Gender-preserving Debiasing for Pre-trained Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1641-1650). Association for Computational Linguistics. doi:10.18653/v1/p19-1160

DOI
10.18653/v1/p19-1160
Conference Paper

Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy.

Alsuhaibani, M., Maehara, T., & Bollegala, D. (2019). Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy.. In AKBC. Retrieved from https://openreview.net/group?id=AKBC.ws/2019/Conference

Conference Paper

Joint Learning of Sense and Word Embeddings

Alsuhaibani, M., & Bollegala, D. (2018). Joint Learning of Sense and Word Embeddings. In PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018) (pp. 223-229). Retrieved from https://www.webofscience.com/

Conference Paper

Learning Relation Representations from Word Representations.

Hakami, H., & Bollegala, D. (2019). Learning Relation Representations from Word Representations.. In AKBC. Retrieved from https://openreview.net/group?id=AKBC.ws/2019/Conference

Conference Paper

Sub-Sequence-Based Dynamic Time Warping

Alshehri, M., Coenen, F., & Dures, K. (2019). Sub-Sequence-Based Dynamic Time Warping. In KDIR: PROCEEDINGS OF THE 11TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL 1: KDIR (pp. 274-281). doi:10.5220/0008053402740281

DOI
10.5220/0008053402740281
Conference Paper

2018

Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

DOI
10.48550/arxiv.1811.00845
Preprint

(OS招待講演)Webからの関係抽出とそれを利用した関係検索

Bollegala, D. (n.d.). (OS招待講演)Webからの関係抽出とそれを利用した関係検索. Unknown Journal, 4k1os25. doi:10.11517/pjsai.jsai2012.0_4k1os25

DOI
10.11517/pjsai.jsai2012.0_4k1os25
Journal article

Webからの人物の属性情報抽出

啓吾, 渡., Bollegala, D., 豊, 松., & 満, 石. (n.d.). Webからの人物の属性情報抽出. Unknown Journal, 3b22. doi:10.11517/pjsai.jsai2009.0_3b22

DOI
10.11517/pjsai.jsai2009.0_3b22
Journal article

多項関係を活用した行為データからの興味予測

のぞみ, 則., Bollegala, D., & 満, 石. (n.d.). 多項関係を活用した行為データからの興味予測. Unknown Journal, 3e3os202. doi:10.11517/pjsai.jsai2011.0_3e3os202

DOI
10.11517/pjsai.jsai2011.0_3e3os202
Journal article

ClassiNet - Predicting Missing Features for Short-Text Classification.

Bollegala, D., Atanasov, V., Maehara, T., & Kawarabayashi, K. -I. (2018). ClassiNet - Predicting Missing Features for Short-Text Classification.. ACM Transactions on Knowledge Discovery from Data, 12(5). doi:10.1145/3201578

DOI
10.1145/3201578
Journal article

Learning Neural Word Salience Scores

Samardzhiev, K., Gargett, A., & Bollegala, D. (2018). Learning Neural Word Salience Scores. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics (pp. 33-42). Association for Computational Linguistics. doi:10.18653/v1/s18-2004

DOI
10.18653/v1/s18-2004
Conference Paper

Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings

DOI
10.48550/arxiv.1804.05262
Preprint

Using $k$-way Co-occurrences for Learning Word Embeddings

Bollegala, D., Yoshida, Y., & Kawarabayashi, K. -I. (2018). Using $k$-way Co-occurrences for Learning Word Embeddings. Proceedings of the National Conference on Artificial Intelligence. Retrieved from http://arxiv.org/abs/1709.01199v1

Journal article

An empirical study on fine-grained named entity recognition

Mai, K., Pham, T. H., Trung, N. M., Duc, N. T., Bolegala, D., Sasano, R., & Sekine, S. (2018). An empirical study on fine-grained named entity recognition. In COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings (pp. 711-722).

Conference Paper

Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings

Coates, J., & Bollegala, D. (2018). Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (pp. 194-198). Association for Computational Linguistics. doi:10.18653/v1/n18-2031

DOI
10.18653/v1/n18-2031
Conference Paper

Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset

Rajendran, P., Bollegala, D., & Parsons, S. (2018). Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (pp. 28-34). Association for Computational Linguistics. doi:10.18653/v1/n18-2005

DOI
10.18653/v1/n18-2005
Conference Paper

Learning word meta-embeddings by autoencoding

Bao, C., & Bollegala, D. (2018). Learning word meta-embeddings by autoencoding. In COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings (pp. 1650-1661).

Conference Paper

Think Globally, Embed Locally - Locally Linear Meta-embedding of Words.

Bollegala, D., Hayashi, K., & Kawarabayashi, K. -I. (2018). Think Globally, Embed Locally - Locally Linear Meta-embedding of Words.. In J. Lang (Ed.), IJCAI (pp. 3970-3976). ijcai.org. Retrieved from http://www.ijcai.org/proceedings/2018/

Conference Paper

Using k-Way Co-Occurrences for Learning Word Embeddings

Bollegala, D., Yoshida, Y., & Kawarabayashi, K. -I. (n.d.). Using k-Way Co-Occurrences for Learning Word Embeddings. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 32. Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v32i1.12010

DOI
10.1609/aaai.v32i1.12010
Conference Paper

Why does PairDiff work? – A mathematical analysis of bilinear relational compositional operators for analogy detection

Hakami, H., Hayashi, K., & Bollegala, D. (2018). Why does PairDiff work? – A mathematical analysis of bilinear relational compositional operators for analogy detection. In COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings (pp. 2493-2504).

Conference Paper

2017

Compositional approaches for representing relations between words: A comparative study.

Hakami, H., & Bollegala, D. (2017). Compositional approaches for representing relations between words: A comparative study.. Knowl.-Based Syst., 136, 172-182.

Journal article

Beyond co-occurrence-based ADR detection from Social Media

Bollegala, D., Maskell, S., & Pirmohamed, M. (2017). Beyond co-occurrence-based ADR detection from Social Media. Poster session presented at the meeting of Unknown Conference. Retrieved from https://www.webofscience.com/

Poster

Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection

DOI
10.48550/arxiv.1709.06673
Preprint

Compositional approaches for representing relations between words: A comparative study

Hakami, H., & Bollegala, D. (2017). Compositional approaches for representing relations between words: A comparative study. KNOWLEDGE-BASED SYSTEMS, 136, 172-182. doi:10.1016/j.knosys.2017.09.008

DOI
10.1016/j.knosys.2017.09.008
Journal article

Compositional Approaches for Representing Relations Between Words: A Comparative Study

DOI
10.48550/arxiv.1709.01193
Preprint

Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint)

Bollegala, D., Maskell, S., Sloane, R., Hajne, J., & Pirmohamed, M. (2017). Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint). doi:10.2196/preprints.8214

DOI
10.2196/preprints.8214
Journal article

CLIEL

García-Constantino, M., Atkinson, K., Bollegala, D., Chapman, K., Coenen, F., Roberts, C., & Robson, K. (2017). CLIEL. In Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law (pp. 79-87). ACM. doi:10.1145/3086512.3086520

DOI
10.1145/3086512.3086520
Conference Paper

Accurate Continuous and Non-intrusive User Authentication with Multivariate Keystroke Streaming

Alshehri, A., Coenen, F., & Bollegala, D. (2017). Accurate Continuous and Non-intrusive User Authentication with Multivariate Keystroke Streaming. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 61-70). SCITEPRESS - Science and Technology Publications. doi:10.5220/0006497200610070

DOI
10.5220/0006497200610070
Conference Paper

User-to-User Recommendation using the Concept of Movement Patterns: A Study using a Dating Social Network

Al-Zeyadi, M., Coenen, F., & Lisitsa, A. (2017). User-to-User Recommendation using the Concept of Movement Patterns: A Study using a Dating Social Network. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 173-180). SCITEPRESS - Science and Technology Publications. doi:10.5220/0006494601730180

DOI
10.5220/0006494601730180
Conference Paper

2016

Cross-domain Sentiment Classification using Sentiment Sensitive Embeddings

Bollegala, D., Mu, T., & Goulermas, Y. (2016). Cross-domain Sentiment Classification using Sentiment Sensitive Embeddings. IEEE Transactions on Knowledge and Data Engineering, 28(02), 389-410. doi:10.1109/TKDE.2015.2475761

DOI
10.1109/TKDE.2015.2475761
Journal article

Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews

Rajendran, P., Bollegala, D., & Parsons, S. (2016). Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews. In Proceedings of the Third Workshop on Argument Mining (ArgMining2016). Association for Computational Linguistics. doi:10.18653/v1/w16-2804

DOI
10.18653/v1/w16-2804
Conference Paper

2015

Joint Word Representation Learning Using a Corpus and a Semantic Lexicon

Bollegala, D., Mohammed, A., Maehara, T., & Kawarabayashi, K. -I. (2016). Joint Word Representation Learning Using a Corpus and a Semantic Lexicon. THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2690-2696. Retrieved from https://www.webofscience.com/

Journal article

Prediction of User Ratings of Oral Presentations using Label Relations

Yamasaki, T., Fukushima, Y., Furuta, R., Sun, L., Aizawa, K., & Bollegala, D. (2015). Prediction of User Ratings of Oral Presentations using Label Relations. In Proceedings of the 1st International Workshop on Affect &amp; Sentiment in Multimedia (pp. 33-38). ACM. doi:10.1145/2813524.2813533

DOI
10.1145/2813524.2813533
Conference Paper

Unsupervised Cross-Domain Word Representation Learning

Bollegala, D., Maehara, T., & Kawarabayashi, K. -I. (2015). Unsupervised Cross-Domain Word Representation Learning. In Proceedings of the conference. Association for Computational Linguistics. Meeting Vol. 1 (pp. 730-740). Beijing, China,. doi:10.3115/v1/P15-1071

DOI
10.3115/v1/P15-1071
Conference Paper

Embedding Semantic Relations into Word Representations

Bollegala, D., Maehara, T., & Kawarabayashi, K. -I. (2015). Embedding Semantic Relations into Word Representations. In PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI) (pp. 1222-1228). Retrieved from https://www.webofscience.com/

Conference Paper

Simultaneous Higher-order Relation Prediction via Collective Incidence Matrix Embedding

Nori, N., Bollegala, D., & Kashima, H. (2015). Simultaneous Higher-order Relation Prediction via Collective Incidence Matrix Embedding. Transactions of the Japanese Society for Artificial Intelligence, 30(2), 459-465. doi:10.1527/tjsai.30.459

DOI
10.1527/tjsai.30.459
Journal article

Embedding Semantic Relations into Word Representations.

Bollegala, D., Maehara, T., & Kawarabayashi, K. -I. (2015). Embedding Semantic Relations into Word Representations.. In Q. Yang, & M. J. Wooldridge (Eds.), IJCAI (pp. 1222-1228). AAAI Press. Retrieved from http://ijcai.org/proceedings/2015

Conference Paper

Interest Prediction via Users' Actions on Social Media

Nori, N., Bollegala, D., & Ishizuka, M. (2015). Interest Prediction via Users' Actions on Social Media. Transactions of the Japanese Society for Artificial Intelligence, 30(4), 613-625. doi:10.1527/tjsai.30_613

DOI
10.1527/tjsai.30_613
Journal article

Unsupervised Cross-Domain Word Representation Learning.

Bollegala, D., Maehara, T., & Kawarabayashi, K. -I. (2015). Unsupervised Cross-Domain Word Representation Learning.. CoRR, abs/1505.07184.

Journal article

2014

Learning Word Representations from Relational Graphs

Bollegala, D., Maehara, T., Yoshida, Y., & Kawarabayashi, K. -I. (2015). Learning Word Representations from Relational Graphs. In PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 2146-2152). Retrieved from https://www.webofscience.com/

Conference Paper

Dynamic Feature Scaling for Online Learning of Binary Classifiers

Bollegala, D. (2017). Dynamic feature scaling for online learning of binary classifiers. KNOWLEDGE-BASED SYSTEMS, 129, 97-105. doi:10.1016/j.knosys.2017.05.010

DOI
10.1016/j.knosys.2017.05.010
Journal article

A Dimension Reduction Approach to Multinomial Relation Prediction

Nori, N., Bollegala, D., & Kashima, H. (2014). A Dimension Reduction Approach to Multinomial Relation Prediction. Transactions of the Japanese Society for Artificial Intelligence, 29(1), 168-176. doi:10.1527/tjsai.29.168

DOI
10.1527/tjsai.29.168
Journal article

Learning Word Representations from Relational Graphs

Bollegala, D., Maehara, T., Yoshida, Y., & Kawarabayashi, K. -I. (n.d.). Learning Word Representations from Relational Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). doi:10.1609/aaai.v29i1.9494

DOI
10.1609/aaai.v29i1.9494
Journal article

Learning to Predict Distributions of Words Across Domains

Bollegala, D., Weir, D., & Carroll, J. (2014). Learning to Predict Distributions of Words Across Domains. In PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1 (pp. 613-623). Retrieved from https://www.webofscience.com/

Conference Paper

2013

Mining for analogous tuples from an entity-relation graph

Bollegala, D., Kusumoto, M., Yoshida, Y., & Kawarabayashi, K. I. (2013). Mining for analogous tuples from an entity-relation graph. In IJCAI International Joint Conference on Artificial Intelligence (pp. 2064-2070).

Conference Paper

Multi-Tweet Summarization of Real-Time Events

Khan, M. A. H., Bollegala, D., Liu, G., & Sezaki, K. (2013). Multi-Tweet Summarization of Real-Time Events. In 2013 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM) (pp. 128-133). doi:10.1109/SocialCom.2013.26

DOI
10.1109/SocialCom.2013.26
Conference Paper

Learning non-linear ranking functions for web search using probabilistic model building GP

Sato, H., Bollegala, D., Hasegawa, Y., & Iba, H. (2013). Learning non-linear ranking functions for web search using probabilistic model building GP. In 2013 IEEE Congress on Evolutionary Computation (pp. 3371-3378). IEEE. doi:10.1109/cec.2013.6557983

DOI
10.1109/cec.2013.6557983
Conference Paper

Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Bollegala, D., Weir, D., & Carroll, J. (2013). Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25(8), 1719-1731. doi:10.1109/tkde.2012.103

DOI
10.1109/tkde.2012.103
Journal article

Minimally Supervised Novel Relation Extraction Using a Latent Relational Mapping

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2013). Minimally Supervised Novel Relation Extraction Using a Latent Relational Mapping. IEEE Transactions on Knowledge and Data Engineering, 25(2), 419-432. doi:10.1109/tkde.2011.250

DOI
10.1109/tkde.2011.250
Journal article

A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization

Bollegala, D., Okazaki, N., & Ishizuka, M. (2013). A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization. In Theory and Applications of Natural Language Processing (pp. 253-276). Springer Berlin Heidelberg. doi:10.1007/978-3-642-28569-1_12

DOI
10.1007/978-3-642-28569-1_12
Chapter

Improving relational similarity measurement using symmetries in proportional word analogies

Bollegala, D., Goto, T., Duc, N. T., & Ishizuka, M. (2013). Improving relational similarity measurement using symmetries in proportional word analogies. Information Processing &amp; Management, 49(1), 355-369. doi:10.1016/j.ipm.2012.05.007

DOI
10.1016/j.ipm.2012.05.007
Journal article

Jointly Learning Similarity Transformations for Textual Entailment

Yokote, K. -I., Bollegala, D., & Ishizuka, M. (2013). Jointly Learning Similarity Transformations for Textual Entailment. Transactions of the Japanese Society for Artificial Intelligence, 28(2), 220-229. doi:10.1527/tjsai.28.220

DOI
10.1527/tjsai.28.220
Journal article

2012

A preference learning approach to sentence ordering for multi-document summarization

Bollegala, D., Okazaki, N., & Ishizuka, M. (2012). A preference learning approach to sentence ordering for multi-document summarization. Information Sciences, 217, 78-95. doi:10.1016/j.ins.2012.06.015

DOI
10.1016/j.ins.2012.06.015
Journal article

A Context Expansion Method for Supervised Word Sense Disambiguation

Tacoa, F., Bollegala, D., & Ishizuka, M. (2012). A Context Expansion Method for Supervised Word Sense Disambiguation. In 2012 IEEE Sixth International Conference on Semantic Computing (pp. 339-341). IEEE. doi:10.1109/icsc.2012.27

DOI
10.1109/icsc.2012.27
Conference Paper

Multinomial Relation Prediction in Social Data: A Dimension Reduction Approach

Nori, N., Bollegala, D., & Kashima, H. (n.d.). Multinomial Relation Prediction in Social Data: A Dimension Reduction Approach. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 26 (pp. 115-121). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v26i1.8110

DOI
10.1609/aaai.v26i1.8110
Conference Paper

Similarity Is Not Entailment — Jointly Learning Similarity Transformation for Textual Entailment

Yokote, K. -I., Bollegala, D., & Ishizuka, M. (n.d.). Similarity Is Not Entailment — Jointly Learning Similarity Transformation for Textual Entailment. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 26 (pp. 1720-1726). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v26i1.8348

DOI
10.1609/aaai.v26i1.8348
Conference Paper

Probabilistic model building GP with Belief propagation

Sato, H., Hasegawa, Y., Bollegala, D., & Iba, H. (2012). Probabilistic model building GP with Belief propagation. In 2012 IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE. doi:10.1109/cec.2012.6256483

DOI
10.1109/cec.2012.6256483
Conference Paper

Cross-Language Latent Relational Search between Japanese and English Languages Using a Web Corpus

Duc, N. T., Bollegala, D., & Ishizuka, M. (2012). Cross-Language Latent Relational Search between Japanese and English Languages Using a Web Corpus. ACM Transactions on Asian Language Information Processing, 11(3), 1-33. doi:10.1145/2334801.2334805

DOI
10.1145/2334801.2334805
Journal article

AUTOMATIC ANNOTATION OF AMBIGUOUS PERSONAL NAMES ON THE WEB

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2012). AUTOMATIC ANNOTATION OF AMBIGUOUS PERSONAL NAMES ON THE WEB. Computational Intelligence, 28(3), 398-425. doi:10.1111/j.1467-8640.2012.00449.x

DOI
10.1111/j.1467-8640.2012.00449.x
Journal article

Improving the Accuracy of Attribute Extraction using the Relatedness between Attribute Values

Bollegala, D., Tani, N., & Ishizuka, M. (2012). Improving the Accuracy of Attribute Extraction using the Relatedness between Attribute Values. Transactions of the Japanese Society for Artificial Intelligence, 27(4), 245-252. doi:10.1527/tjsai.27.245

DOI
10.1527/tjsai.27.245
Journal article

Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy

BOLLEGALA, D., MATSUO, Y., & ISHIZUKA, M. (2012). Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy. IEICE Transactions on Information and Systems, E95.D(8), 2116-2123. doi:10.1587/transinf.e95.d.2116

DOI
10.1587/transinf.e95.d.2116
Journal article

Multinomial Relation Prediction in Social Data: A Dimension Reduction Approach

Nori, N., Bollegala, D., & Kashima, H. (2012). Multinomial Relation Prediction in Social Data: A Dimension Reduction Approach. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 115-121).

Conference Paper

Similarity Is Not Entailment - Jointly Learning Similarity Transformations for Textual Entailment

Yokote, K. I., Bollegala, D., & Ishizuka, M. (2012). Similarity Is Not Entailment - Jointly Learning Similarity Transformations for Textual Entailment. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 1720-1726).

Conference Paper

2011

Interest prediction on multinomial, time-evolving social graphs

Nori, N., Bollegala, D., & Ishizuka, M. (2011). Interest prediction on multinomial, time-evolving social graphs. In IJCAI International Joint Conference on Artificial Intelligence (pp. 2507-2512). doi:10.5591/978-1-57735-516-8/IJCAI11-417

DOI
10.5591/978-1-57735-516-8/IJCAI11-417
Conference Paper

Relation adaptation: Learning to extract novel relations with minimum supervision

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011). Relation adaptation: Learning to extract novel relations with minimum supervision. In IJCAI International Joint Conference on Artificial Intelligence (pp. 2205-2210). doi:10.5591/978-1-57735-516-8/IJCAI11-368

DOI
10.5591/978-1-57735-516-8/IJCAI11-368
Conference Paper

Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification

Bollegala, D., Weir, D., & Carroll, J. (2011). Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. In ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies Vol. 1 (pp. 132-141).

Conference Paper

Cross-Language Latent Relational Search: Mapping Knowledge across Languages

Tuan Duc, N., Bollegala, D., & Ishizuka, M. (n.d.). Cross-Language Latent Relational Search: Mapping Knowledge across Languages. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 25 (pp. 1237-1242). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v25i1.8075

DOI
10.1609/aaai.v25i1.8075
Conference Paper

Collaborative exploratory search in real-world context

Tani, N., Bollegala, D., Chandrasiri, N., Okamoto, K., Nawa, K., Iitsuka, S., & Matsuo, Y. (2011). Collaborative exploratory search in real-world context. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 2137-2140). ACM. doi:10.1145/2063576.2063909

DOI
10.1145/2063576.2063909
Conference Paper

Improving Relational Search Performance using Relational Symmetries and Predictors

Goto, T., Tuan Duc, N., Danushka, B., & Ishizuka, M. (2011). Improving Relational Search Performance using Relational Symmetries and Predictors. Transactions of the Japanese Society for Artificial Intelligence, 26(6), 649-656. doi:10.1527/tjsai.26.649

DOI
10.1527/tjsai.26.649
Journal article

An adaptive differential evolution algorithm

Noman, N., Bollegala, D., & Iba, H. (2011). An adaptive differential evolution algorithm. In 2011 IEEE Congress of Evolutionary Computation (CEC) (pp. 2229-2236). IEEE. doi:10.1109/cec.2011.5949891

DOI
10.1109/cec.2011.5949891
Conference Paper

Cross-Language Latent Relational Search: Mapping Knowledge across Languages

Duc, N. T., Bollegala, D., & Ishizuka, M. (2011). Cross-Language Latent Relational Search: Mapping Knowledge across Languages. In Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011 (pp. 1237-1242).

Conference Paper

Exploiting User Interest on Social Media for Aggregating Diverse Data and Predicting Interest

Nori, N., Bollegala, D., & Ishizuka, M. (n.d.). Exploiting User Interest on Social Media for Aggregating Diverse Data and Predicting Interest. In Proceedings of the International AAAI Conference on Web and Social Media Vol. 5 (pp. 241-248). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/icwsm.v5i1.14114

DOI
10.1609/icwsm.v5i1.14114
Conference Paper

Differential evolution with self adaptive local search

Noman, N., Bollegala, D., & Iba, H. (2011). Differential evolution with self adaptive local search. In Proceedings of the 13th annual conference on Genetic and evolutionary computation. ACM. doi:10.1145/2001576.2001725

DOI
10.1145/2001576.2001725
Conference Paper

RankDE

Bollegala, D., Noman, N., & Iba, H. (2011). RankDE. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (pp. 1771-1778). ACM. doi:10.1145/2001576.2001814

DOI
10.1145/2001576.2001814
Conference Paper

Total Environment for Text Data Mining

Sunayama, W., Takama, Y., Bollegala, D., Nishihara, Y., Tokunaga, H., Kushima, M., & Matsushita, M. (2011). Total Environment for Text Data Mining. Transactions of the Japanese Society for Artificial Intelligence, 26(4), 483-493. doi:10.1527/tjsai.26.483

DOI
10.1527/tjsai.26.483
Journal article

From actors, politicians, to CEOs

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011). From actors, politicians, to CEOs. In Proceedings of the 20th international conference companion on World wide web (pp. 13-14). ACM. doi:10.1145/1963192.1963200

DOI
10.1145/1963192.1963200
Conference Paper

Automatic Discovery of Personal Name Aliases from the Web

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011). Automatic Discovery of Personal Name Aliases from the Web. IEEE Transactions on Knowledge and Data Engineering, 23(6), 831-844. doi:10.1109/tkde.2010.162

DOI
10.1109/tkde.2010.162
Journal article

Semi-supervised Discourse Relation Classification with Structural Learning

Hernault, H., Bollegala, D., & Ishizuka, M. (2011). Semi-supervised Discourse Relation Classification with Structural Learning. In Unknown Conference (pp. 340-352). Springer Berlin Heidelberg. doi:10.1007/978-3-642-19400-9_27

DOI
10.1007/978-3-642-19400-9_27
Conference Paper

Using Graph Based Method to Improve Bootstrapping Relation Extraction

Li, H., Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011). Using Graph Based Method to Improve Bootstrapping Relation Extraction. In Unknown Conference (pp. 127-138). Springer Berlin Heidelberg. doi:10.1007/978-3-642-19437-5_10

DOI
10.1007/978-3-642-19437-5_10
Conference Paper

Relation Representation and Indexing Method for a Fast and High Precision Latent Relational Web Search Engine

Tuan Duc, N., Bollegala, D., & Ishizuka, M. (2011). Relation Representation and Indexing Method for a Fast and High Precision Latent Relational Web Search Engine. Transactions of the Japanese Society for Artificial Intelligence, 26(2), 307-312. doi:10.1527/tjsai.26.307

DOI
10.1527/tjsai.26.307
Journal article

A Supervised Classification Approach for Measuring Relational Similarity between Word Pairs

BOLLEGALA, D., MATSUO, Y., & ISHIZUKA, M. (2011). A Supervised Classification Approach for Measuring Relational Similarity between Word Pairs. IEICE Transactions on Information and Systems, E94-D(11), 2227-2233. doi:10.1587/transinf.e94.d.2227

DOI
10.1587/transinf.e94.d.2227
Journal article

A Web Search Engine-Based Approach to Measure Semantic Similarity between Words

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011). A Web Search Engine-Based Approach to Measure Semantic Similarity between Words. IEEE Transactions on Knowledge and Data Engineering, 23(7), 977-990. doi:10.1109/tkde.2010.172

DOI
10.1109/tkde.2010.172
Journal article

Automatic Extraction of Related Terms using Web Search Engines

WATANABE, K., BOLLEGALA, D., MATSUO, Y., & ISHIZUKA, M. (2011). Automatic Extraction of Related Terms using Web Search Engines. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 23(5), 739-748. doi:10.3156/jsoft.23.739

DOI
10.3156/jsoft.23.739
Journal article

2010

A Sequential Model for Discourse Segmentation

Hernault, H., Bollegala, D., & Ishizuka, M. (2010). A Sequential Model for Discourse Segmentation. In Unknown Conference (pp. 315-326). Springer Berlin Heidelberg. doi:10.1007/978-3-642-12116-6_26

DOI
10.1007/978-3-642-12116-6_26
Conference Paper

Using Relational Similarity between Word Pairs for Latent Relational Search on the Web

Duc, N. T., Bollegala, D., & Ishizuka, M. (2010). Using Relational Similarity between Word Pairs for Latent Relational Search on the Web. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 196-199). IEEE. doi:10.1109/wi-iat.2010.167

DOI
10.1109/wi-iat.2010.167
Conference Paper

A semi-supervised approach to improve classification of infrequent discourse relations using feature vector extension

Hernault, H., Bollegala, D., & Ishizuka, M. (2010). A semi-supervised approach to improve classification of infrequent discourse relations using feature vector extension. In EMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 399-409).

Conference Paper

A supervised ranking approach for detecting relationally similar word pairs

Bollegala, D. (2010). A supervised ranking approach for detecting relationally similar word pairs. In 2010 Fifth International Conference on Information and Automation for Sustainability (pp. 323-328). IEEE. doi:10.1109/iciafs.2010.5715681

DOI
10.1109/iciafs.2010.5715681
Conference Paper

Towards semi-supervised classification of discourse relations using feature correlations

Hernault, H., Bollegala, D., & Ishizuka, M. (2010). Towards semi-supervised classification of discourse relations using feature correlations. In Proceedings of the SIGDIAL 2010 Conference: 11th Annual Meeting of the Special Interest Group onDiscourse and Dialogue (pp. 55-58).

Conference Paper

Exploiting Symmetry in Relational Similarity for Ranking Relational Search Results

Goto, T., Duc, N. T., Bollegala, D., & Ishizuka, M. (2010). Exploiting Symmetry in Relational Similarity for Ranking Relational Search Results. In Unknown Conference (pp. 595-600). Springer Berlin Heidelberg. doi:10.1007/978-3-642-15246-7_55

DOI
10.1007/978-3-642-15246-7_55
Conference Paper

Relational duality

Bollegala, D. T., Matsuo, Y., & Ishizuka, M. (2010). Relational duality. In Proceedings of the 19th international conference on World wide web (pp. 151-160). ACM. doi:10.1145/1772690.1772707

DOI
10.1145/1772690.1772707
Conference Paper

A bottom-up approach to sentence ordering for multi-document summarization

Bollegala, D., Okazaki, N., & Ishizuka, M. (2010). A bottom-up approach to sentence ordering for multi-document summarization. Information Processing &amp; Management, 46(1), 89-109. doi:10.1016/j.ipm.2009.07.004

DOI
10.1016/j.ipm.2009.07.004
Journal article

2009

Measuring the similarity between implicit semantic relations from the web

Bollegala, D. T., Matsuo, Y., & Ishizuka, M. (2009). Measuring the similarity between implicit semantic relations from the web. In Proceedings of the 18th international conference on World wide web (pp. 651-660). ACM. doi:10.1145/1526709.1526797

DOI
10.1145/1526709.1526797
Conference Paper

A study on attributional and relational similarity between word pairs on the Web

BOLLEGALA, D. (2009). A study on attributional and relational similarity between word pairs on the Web. doi:10.15083/00002426

DOI
10.15083/00002426
Journal article

Measuring the similarity between implicit semantic relations using web search engines

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2009). Measuring the similarity between implicit semantic relations using web search engines. In Proceedings of the Second ACM International Conference on Web Search and Data Mining (pp. 104-113). ACM. doi:10.1145/1498759.1498815

DOI
10.1145/1498759.1498815
Conference Paper

A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2009). A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP '09 Vol. 2 (pp. 803). Association for Computational Linguistics. doi:10.3115/1699571.1699617

DOI
10.3115/1699571.1699617
Conference Paper

2008

Automatically Extracting Personal Name Aliases from the Web

Bollegala, D., Honma, T., Matsuo, Y., & Ishizuka, M. (2008). Automatically Extracting Personal Name Aliases from the Web. In Unknown Conference (pp. 77-88). Springer Berlin Heidelberg. doi:10.1007/978-3-540-85287-2_8

DOI
10.1007/978-3-540-85287-2_8
Conference Paper

WWW sits the SAT: Measuring relational similarity on the web

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2008). WWW sits the SAT: Measuring relational similarity on the web. In Frontiers in Artificial Intelligence and Applications Vol. 178 (pp. 333-337). doi:10.3233/978-1-58603-891-5-333

DOI
10.3233/978-1-58603-891-5-333
Conference Paper

Mining for personal name aliases on the web

Bollegala, D., Honma, T., Matsuo, Y., & Ishizuka, M. (2008). Mining for personal name aliases on the web. In Proceedings of the 17th international conference on World Wide Web (pp. 1107-1108). ACM. doi:10.1145/1367497.1367679

DOI
10.1145/1367497.1367679
Conference Paper

A Co-occurrence graph-based approach for personal name alias extraction from anchor texts

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2008). A Co-occurrence graph-based approach for personal name alias extraction from anchor texts. In IJCNLP 2008 - 3rd International Joint Conference on Natural Language Processing, Proceedings of the Conference Vol. 2 (pp. 865-870).

Conference Paper

2007

An integrated approach to measuring semantic similarity between words using information available on the Web

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2007). An integrated approach to measuring semantic similarity between words using information available on the Web. In NAACL HLT 2007 - Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference (pp. 340-347).

Conference Paper

Measuring semantic similarity between words using web search engines

Measuring semantic similarity between words using web search engines (2007). In Proceedings of the 16th international conference on World Wide Web (pp. 757-766). ACM. doi:10.1145/1242572.1242675

DOI
10.1145/1242572.1242675
Conference Paper

2006

Spinning multiple social networks for Semantic Web

Matsuo, Y., Hamasaki, M., Nakamura, Y., Nishimura, T., Hasida, K., Takeda, H., . . . Ishizuka, M. (2006). Spinning multiple social networks for Semantic Web. In Proceedings of the National Conference on Artificial Intelligence Vol. 2 (pp. 1381-1387).

Conference Paper

Extracting Key Phrases to Disambiguate Personal Names on the Web

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2006). Extracting Key Phrases to Disambiguate Personal Names on the Web. In Unknown Conference (pp. 223-234). Springer Berlin Heidelberg. doi:10.1007/11671299_24

DOI
10.1007/11671299_24
Conference Paper

A bottom-up approach to sentence ordering for multi-document summarization

Bollegala, D., Okazaki, N., & Ishizuka, M. (2006). A bottom-up approach to sentence ordering for multi-document summarization. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 (pp. 385-392). Association for Computational Linguistics. doi:10.3115/1220175.1220224

DOI
10.3115/1220175.1220224
Conference Paper

Disambiguating personal names on the web using automatically extracted key phrases

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2006). Disambiguating personal names on the web using automatically extracted key phrases. Frontiers in Artificial Intelligence and Applications, 141, 553-557.

Journal article

Extracting key phrases to disambiguate personal name queries in web search

Bollegala, D., Matsuo, Y., & Ishizuka, M. (2006). Extracting key phrases to disambiguate personal name queries in web search. In Proceedings of the Workshop on How Can Computational Linguistics Improve Information Retrieval? - CLIIR '06 (pp. 17). Association for Computational Linguistics. doi:10.3115/1629808.1629812

DOI
10.3115/1629808.1629812
Conference Paper

2005

A Machine Learning Approach to Sentence Ordering for Multidocument Summarization and Its Evaluation

Bollegala, D., Okazaki, N., & Ishizuka, M. (2005). A Machine Learning Approach to Sentence Ordering for Multidocument Summarization and Its Evaluation. In Unknown Conference (pp. 624-635). Springer Berlin Heidelberg. doi:10.1007/11562214_55

DOI
10.1007/11562214_55
Conference Paper