Photo of Prof Danushka Bollegala

Prof Danushka Bollegala

Professor Computer Science

    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)

    2020

    Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction (Journal article)

    Bollegala, D., Kiryo, R., Tsujino, K., & Yukawa, H. (n.d.). Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction. Retrieved from http://arxiv.org/abs/2002.11004v1

    2019

    Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA (Journal article)

    Fain, M., Ponikar, A., Fox, R., & Bollegala, D. (n.d.). Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA. Retrieved from http://arxiv.org/abs/1911.12763v1

    Anonymising Queries by Semantic Decomposition (Journal article)

    Bollegala, D., Machide, T., & Kawarabayashi, K. -I. (n.d.). Anonymising Queries by Semantic Decomposition. Retrieved from http://arxiv.org/abs/1909.05819v1

    Transfer Reward Learning for Policy Gradient-Based Text Generation (Journal article)

    Neill, J. O., & Bollegala, D. (n.d.). Transfer Reward Learning for Policy Gradient-Based Text Generation. Retrieved from http://arxiv.org/abs/1909.03622v1

    Self-Adaptation for Unsupervised Domain Adaptation (Conference Paper)

    Cui, X., & Bollegala, D. (2019). Self-Adaptation for Unsupervised Domain Adaptation. In Proceedings - Natural Language Processing in a Deep Learning World. Incoma Ltd., Shoumen, Bulgaria. doi:10.26615/978-954-452-056-4_025

    DOI: 10.26615/978-954-452-056-4_025

    Correcting crowdsourced annotations to improve detection of outcome types in evidence based medicine (Conference Paper)

    Abaho, M., Bollegala, D., Williamson, P., & Dodd, S. (2019). Correcting crowdsourced annotations to improve detection of outcome types in evidence based medicine. In CEUR Workshop Proceedings Vol. 2429 (pp. 1-5).

    Tick parasitism classification from noisy medical records (Conference Paper)

    Neill, J. O., Bollegala, D., Radford, A. D., & Noble, P. J. (2019). Tick parasitism classification from noisy medical records. In CEUR Workshop Proceedings Vol. 2429 (pp. 30-34).

    Gender-preserving Debiasing for Pre-trained Word Embeddings (Conference Paper)

    Masahiro, K., & Bollegala, D. (2019). Gender-preserving Debiasing for Pre-trained Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy.

    Automated Bundle Pagination Using Machine Learning (Conference Paper)

    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 - ICAIL '19. ACM Press. doi:10.1145/3322640.3326726

    DOI: 10.1145/3322640.3326726

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

    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

    Error-Correcting Neural Sequence Prediction (Journal article)

    Neill, J. O., & Bollegala, D. (n.d.). Error-Correcting Neural Sequence Prediction. Retrieved from http://arxiv.org/abs/1901.07002v2

    "Touching to See" and "Seeing to Feel": Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception (Conference Paper)

    Lee, J. -T., Bollegala, D., Luo, S., & IEEE. (2019). "Touching to See" and "Seeing to Feel": Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception. In 2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) (pp. 4276-4282). Retrieved from http://gateway.webofknowledge.com/

    A dataset for inter-sentence relation extraction using distant supervision (Conference Paper)

    Mandya, A., Bollegala, D., Coenen, F., & Atkinson, K. (2019). A dataset for inter-sentence relation extraction using distant supervision. In LREC 2018 - 11th International Conference on Language Resources and Evaluation (pp. 1559-1565).

    Combining textual and visual information for typed and handwritten text separation in legal documents (Conference Paper)

    Torrisi, A., Bevan, R., Atkinson, K., Bollegala, D., & Coenen, F. (2019). Combining textual and visual information for typed and handwritten text separation in legal documents. In Frontiers in Artificial Intelligence and Applications Vol. 322 (pp. 223-228). doi:10.3233/FAIA190329

    DOI: 10.3233/FAIA190329

    Gender-preserving Debiasing for Pre-trained Word Embeddings. (Conference Paper)

    Kaneko, M., & Bollegala, D. (2019). Gender-preserving Debiasing for Pre-trained Word Embeddings.. In ACL (1) (pp. 1641-1650).

    Graph Matching Based Semantic Search Engine (Conference Paper)

    Farouk, M., Ishizuka, M., & Bollegala, D. (2019). Graph Matching Based Semantic Search Engine. In Unknown Conference (pp. 89-100). Springer International Publishing. doi:10.1007/978-3-030-14401-2_8

    DOI: 10.1007/978-3-030-14401-2_8

    Joint learning of sense and word embeddings (Conference Paper)

    Alsuhaibani, M., & Bollegala, D. (2019). Joint learning of sense and word embeddings. In LREC 2018 - 11th International Conference on Language Resources and Evaluation (pp. 223-229).

    Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition. (Journal article)

    Pham, T. -H., Mai, K., Nguyen, M. T., Duc, N. T., Bollegala, D., Sasano, R., & Sekine, S. (2019). Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition.. CoRR, abs/1902.10118.

    Sentiment-stance-specificity (SSS) dataset: Identifying support-based entailment among opinions (Conference Paper)

    Rajendran, P., Bollegala, D., & Parsons, S. (2019). Sentiment-stance-specificity (SSS) dataset: Identifying support-based entailment among opinions. In LREC 2018 - 11th International Conference on Language Resources and Evaluation (pp. 619-626).

    Sub-sequence-based dynamic time warping (Conference Paper)

    Alshehri, M., Coenen, F., & Dures, K. (2019). Sub-sequence-based dynamic time warping. In IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management Vol. 1 (pp. 274-281).

    Unsupervised Evaluation of Human Translation Quality (Conference Paper)

    Zhou, Y., & Bollegala, D. (2019). Unsupervised Evaluation of Human Translation Quality. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SCITEPRESS - Science and Technology Publications. doi:10.5220/0008064500550064

    DOI: 10.5220/0008064500550064

    2018

    Analysing Dropout and Compounding Errors in Neural Language Models (Journal article)

    Neill, J. O., & Bollegala, D. (n.d.). Analysing Dropout and Compounding Errors in Neural Language Models. Retrieved from http://arxiv.org/abs/1811.00998v1

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

    Mandya, A., Bollegala, D., Coenen, F., & Atkinson, K. (n.d.). Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction. Retrieved from http://arxiv.org/abs/1811.00845v1

    Efficient and Effective Case Reject-Accept Filtering: A Study Using Machine Learning (Conference Paper)

    Coenen, F. P., Bevan, R., Torrisi, A., Atkinson, K., & Bollegala, D. (n.d.). Efficient and Effective Case Reject-Accept Filtering: A Study Using Machine Learning. In JURIX 2018.

    Curriculum-Based Neighborhood Sampling For Sequence Prediction (Journal article)

    Neill, J. O., & Bollegala, D. (n.d.). Curriculum-Based Neighborhood Sampling For Sequence Prediction. Retrieved from http://arxiv.org/abs/1809.05916v1

    Meta-Embedding as Auxiliary Task Regularization (Journal article)

    Neill, J. O., & Bollegala, D. (n.d.). Meta-Embedding as Auxiliary Task Regularization. Retrieved from http://arxiv.org/abs/1809.05886v2

    Angular-Based Word Meta-Embedding Learning (Journal article)

    Neill, J. O., & Bollegala, D. (n.d.). Angular-Based Word Meta-Embedding Learning. Retrieved from http://arxiv.org/abs/1808.04334v1

    ClassiNet - Predicting Missing Features for Short-Text Classification. (Journal article)

    Bollegala, D., Atanasov, V., Maehara, T., & Kawarabayashi, K. -I. (2018). ClassiNet - Predicting Missing Features for Short-Text Classification.. TKDD, 12, 55:1.

    Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings (Conference Paper)

    Coates, J., & Bollegala, D. (n.d.). Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings. Retrieved from http://arxiv.org/abs/1804.05262v1

    Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset. (Conference Paper)

    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 M. A. Walker, H. Ji, & A. Stent (Eds.), NAACL-HLT (2) (pp. 28-34). Association for Computational Linguistics. Retrieved from https://aclanthology.info/

    Learning Neural Word Salience Scores. (Conference Paper)

    Samardzhiev, K., Gargett, A., & Bollegala, D. (2018). Learning Neural Word Salience Scores.. In M. Nissim, J. Berant, & A. Lenci (Eds.), *SEM@NAACL-HLT (pp. 33-42). Association for Computational Linguistics. Retrieved from https://aclanthology.info/

    Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition. (Conference Paper)

    Cui, X., Kojaku, S., Masuda, N., & Bollegala, D. (2018). Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition.. In M. Nissim, J. Berant, & A. Lenci (Eds.), *SEM@NAACL-HLT (pp. 255-264). Association for Computational Linguistics. Retrieved from https://aclanthology.info/

    ClassiNet -- Predicting Missing Features for Short-Text Classification (Journal article)

    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.

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

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

    DOI: 10.2196/preprints.8214

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

    Bollegala, D., Maskell, S., Sloane, R., Hajne, J., & Pirmohamed, M. (2018). Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach.. JMIR public health and surveillance, 4(2), e51. doi:10.2196/publichealth.8214

    DOI: 10.2196/publichealth.8214

    Iterative Keystroke Continuous Authentication: A Time Series Based Approach (Journal article)

    Alshehri, A., Coenen, F., & Bollegala, D. (2018). Iterative Keystroke Continuous Authentication: A Time Series Based Approach. KI - Künstliche Intelligenz, 32(4), 231-243. doi:10.1007/s13218-018-0526-z

    DOI: 10.1007/s13218-018-0526-z

    Dropping Networks for Transfer Learning (Journal article)

    Neill, J. O., & Bollegala, D. (n.d.). Dropping Networks for Transfer Learning. Retrieved from http://arxiv.org/abs/1804.08501v3

    A Comparative Study of Pivot Selection Strategies for Unsupervised Domain Adaptation (Journal article)

    Cui, X., Al-Bazzas, N., Bollegala, D., & Coenen, F. P. (2018). A Comparative Study of Pivot Selection Strategies for Unsupervised Domain Adaptation. The Knowledge Engineering Review.

    Jointly learning word embeddings using a corpus and a knowledge base (Journal article)

    Alsuhaibani, M., Bollegala, D., Maehara, T., & Kawarabayashi, K. -I. (2018). Jointly learning word embeddings using a corpus and a knowledge base. PLOS ONE, 13(3). doi:10.1371/journal.pone.0193094

    DOI: 10.1371/journal.pone.0193094

    Frame-Based Semantic Patterns for Relation Extraction (Conference Paper)

    Mandya, A., Bollegala, D., Coenen, F., & Atkinson, K. (2018). Frame-Based Semantic Patterns for Relation Extraction. In Unknown Conference (pp. 51-62). Springer Singapore. doi:10.1007/978-981-10-8438-6_5

    DOI: 10.1007/978-981-10-8438-6_5

    Using $k$-way Co-occurrences for Learning Word Embeddings (Journal article)

    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

    An Empirical Study on Fine-Grained Named Entity Recognition. (Conference Paper)

    Mai, K., Pham, T. -H., Nguyen, M. T., Duc, N. T., Bollegala, D., Sasano, R., & Sekine, S. (2018). An Empirical Study on Fine-Grained Named Entity Recognition.. In E. M. Bender, L. Derczynski, & P. Isabelle (Eds.), COLING (pp. 711-722). Association for Computational Linguistics. Retrieved from https://aclanthology.info/

    Frustratingly Easy Meta-Embedding - Computing Meta-Embeddings by Averaging Source Word Embeddings. (Conference Paper)

    Coates, J., & Bollegala, D. (2018). Frustratingly Easy Meta-Embedding - Computing Meta-Embeddings by Averaging Source Word Embeddings.. In M. A. Walker, H. Ji, & A. Stent (Eds.), NAACL-HLT (2) (pp. 194-198). Association for Computational Linguistics. Retrieved from https://aclanthology.info/

    Learning Word Meta-Embeddings by Autoencoding. (Conference Paper)

    Bollegala, D., & Bao, C. (2018). Learning Word Meta-Embeddings by Autoencoding.. In E. M. Bender, L. Derczynski, & P. Isabelle (Eds.), COLING (pp. 1650-1661). Association for Computational Linguistics. Retrieved from https://aclanthology.info/

    Spectral analysis of keystroke streams: Towards effective real-time continuous user authentication (Conference Paper)

    Alshehri, A., Coenen, F., & Bollegala, D. (2018). Spectral analysis of keystroke streams: Towards effective real-time continuous user authentication. In ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy Vol. 2018-January (pp. 62-73).

    Think Globally, Embed Locally - Locally Linear Meta-embedding of Words. (Conference Paper)

    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/

    Using k-Way Co-Occurrences for Learning Word Embeddings. (Conference Paper)

    Bollegala, D., Yoshida, Y., & Kawarabayashi, K. -I. (2018). Using k-Way Co-Occurrences for Learning Word Embeddings.. In S. A. McIlraith, & K. Q. Weinberger (Eds.), AAAI (pp. 5037-5044). AAAI Press. Retrieved from https://www.aaai.org/ocs/index.php/AAAI/AAAI18/schedConf/presentations

    Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection. (Conference Paper)

    Hakami, H., Hayashi, K., & Bollegala, D. (2018). Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection.. In E. M. Bender, L. Derczynski, & P. Isabelle (Eds.), COLING (pp. 2493-2504). Association for Computational Linguistics. Retrieved from https://aclanthology.info/

    2017

    TSP: Learning Task-Specific Pivots for Unsupervised Domain Adaptation (Conference Paper)

    Coenen, F. P., Cui., & bollegala. (2017). TSP: Learning Task-Specific Pivots for Unsupervised Domain Adaptation. In ECML-PKDD.

    Spectral Keyboard Streams: Towards Effective and Continuous Authentication (Conference Paper)

    Coenen, F. P., alshehri., & bollegala. (2017). Spectral Keyboard Streams: Towards Effective and Continuous Authentication.

    Compositional approaches for representing relations between words: A comparative study (Journal article)

    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

    Compositional approaches for representing relations between words: A comparative study. (Journal article)

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

    Beyond co-occurrence-based ADR detection from Social Media (Poster)

    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 http://gateway.webofknowledge.com/

    Learning linear transformations between counting-based and prediction-based word embeddings (Journal article)

    Bollegala, D., Hayashi, K., & Kawarabayashi, K. -I. (2017). Learning linear transformations between counting-based and prediction-based word embeddings. PLOS ONE, 12(9). doi:10.1371/journal.pone.0184544

    DOI: 10.1371/journal.pone.0184544

    Discovering Representative Space for Relational Similarity Measurement (Conference Paper)

    Hakami, H., Mandya, A., & Bollegala, D. (2018). Discovering Representative Space for Relational Similarity Measurement. In Unknown Conference (pp. 76-87). Springer Singapore. doi:10.1007/978-981-10-8438-6_7

    DOI: 10.1007/978-981-10-8438-6_7

    Identifying Argument based Relation Properties in Opinions (Conference Paper)

    Rajendran., Bollegala, D., & Parsons. (2017). Identifying Argument based Relation Properties in Opinions. In Springer LNCS. Myanmar.

    An iterative approach for the global estimation of sentence similarity (Journal article)

    Kajiwara, T., Bollegala, D., Yoshida, Y., & Kawarabayashi, K. -I. (2017). An iterative approach for the global estimation of sentence similarity. PLOS ONE, 12(9). doi:10.1371/journal.pone.0180885

    DOI: 10.1371/journal.pone.0180885

    Dynamic Feature Scaling for Online Learning of Binary Classifiers (Journal article)

    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

    Dynamic feature scaling for online learning of binary classifiers (Journal article)

    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

    Classifier-Based Pattern Selection Approach for Relation Instance Extraction (Conference Paper)

    Mandya, A., Bollegala, D., Coenen, F., & Atkinson, K. (2018). Classifier-Based Pattern Selection Approach for Relation Instance Extraction. In COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING (CICLING 2017), PT I Vol. 10761 (pp. 418-434). doi:10.1007/978-3-319-77113-7_33

    DOI: 10.1007/978-3-319-77113-7_33

    Accurate continuous and non-intrusive user authentication with multivariate keystroke streaming (Conference Paper)

    Alshehri, A., Coenen, F., & Bollegala, D. (2017). Accurate continuous and non-intrusive user authentication with multivariate keystroke streaming. In IC3K 2017 - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management Vol. 1 (pp. 61-70).

    CLIEL (Conference Paper)

    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 - ICAIL '17. ACM Press. doi:10.1145/3086512.3086520

    DOI: 10.1145/3086512.3086520

    Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection Strategies. (Conference Paper)

    Cui, X., Coenen, F., & Bollegala, D. (2017). Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection Strategies.. In LIDTA@PKDD/ECML Vol. 74 (pp. 103-115). PMLR. Retrieved from http://proceedings.mlr.press/v74/

    User-to-user recommendation using the concept of movement patterns: A study using a dating social network (Conference Paper)

    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 IC3K 2017 - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management Vol. 1 (pp. 173-180).

    2016

    Towards Keystroke Continuous Authentication Using Time Series Analytics. (Conference Paper)

    Alshehri, A., Coenen, F., & Bollegala, D. (2016). Towards Keystroke Continuous Authentication Using Time Series Analytics.. In M. Bramer, & M. Petridis (Eds.), SGAI Conf. (pp. 325-339). Springer. Retrieved from https://doi.org/10.1007/978-3-319-47175-4

    Keyboard Usage Authentication using Multi-variant Time Series Analysis (Conference Paper)

    Coenen, F. P., Alshehri., & Bollegala. (2016). Keyboard Usage Authentication using Multi-variant Time Series Analysis. In Springer LNCS. Porto, Portugal.

    Assessing Weight of Opinion by Aggregating Coalitions of Arguments (Conference Paper)

    Rajendran, P., Bollegala, D., & Parsons, S. (2016). Assessing Weight of Opinion by Aggregating Coalitions of Arguments. In COMPUTATIONAL MODELS OF ARGUMENT Vol. 287 (pp. 431-438). doi:10.3233/978-1-61499-686-6-431

    DOI: 10.3233/978-1-61499-686-6-431

    Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews. (Conference Paper)

    Rajendran, P., Bollegala, D., & Parsons, S. (2016). Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews.. In ArgMining@ACL. The Association for Computer Linguistics. Retrieved from http://aclweb.org/anthology/W/W16/

    Joint Word Representation Learning Using a Corpus and a Semantic Lexicon. (Conference Paper)

    Bollegala, D., Alsuhaibani, M., Maehara, T., & Kawarabayashi, K. -I. (2016). Joint Word Representation Learning Using a Corpus and a Semantic Lexicon.. In D. Schuurmans, & M. P. Wellman (Eds.), AAAI (pp. 2690-2696). AAAI Press. Retrieved from http://www.aaai.org/Library/AAAI/aaai16contents.php

    2015

    A classification approach for detecting cross-lingual biomedical term translations (Journal article)

    Hakami, H., & Bollegala, D. (2017). A classification approach for detecting cross-lingual biomedical term translations. NATURAL LANGUAGE ENGINEERING, 23(1), 31-51. doi:10.1017/S1351324915000431

    DOI: 10.1017/S1351324915000431

    Prediction of user ratings of oral presentations using label relations (Conference Paper)

    Yamasaki, T., Fukushima, Y., Furuta, R., Sun, L., Aizawa, K., & Bollegala, D. (2015). Prediction of user ratings of oral presentations using label relations. In ASM 2015 - Proceedings of the 1st International Workshop on Affect and Sentiment in Multimedia, co-located with ACM MM 2015 (pp. 33-38). doi:10.1145/2813524.2813533

    DOI: 10.1145/2813524.2813533

    Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings (Journal article)

    Bollegala, D., Mu, T., & Goulermas, J. Y. (2016). Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 28(2), 398-410. doi:10.1109/TKDE.2015.2475761

    DOI: 10.1109/TKDE.2015.2475761

    Improved sampling using loopy belief propagation for probabilistic model building genetic programming (Journal article)

    Sato, H., Hasegawa, Y., Bollegala, D., & Iba, H. (2015). Improved sampling using loopy belief propagation for probabilistic model building genetic programming. SWARM AND EVOLUTIONARY COMPUTATION, 23, 1-10. doi:10.1016/j.swevo.2015.02.002

    DOI: 10.1016/j.swevo.2015.02.002

    Embedding Semantic Relations into Word Representations (Conference Paper)

    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 http://gateway.webofknowledge.com/

    Social media and pharmacovigilance: A review of the opportunities and challenges (Journal article)

    Sloane, R., Osanlou, O., Lewis, D., Bollegala, D., Maskell, S., & Pirmohamed, M. (2015). Social media and pharmacovigilance: A review of the opportunities and challenges. BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 80(4), 910-920. doi:10.1111/bcp.12717

    DOI: 10.1111/bcp.12717

    A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations (Journal article)

    Bollegala, D., Kontonatsios, G., & Ananiadou, S. (2015). A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations. PLOS ONE, 10(6). doi:10.1371/journal.pone.0126196

    DOI: 10.1371/journal.pone.0126196

    A discourse search engine based on rhetorical structure theory (Conference Paper)

    Kuyten, P., Bollegala, D., Hollerit, B., Prendinger, H., & Aizawa, K. (2015). A discourse search engine based on rhetorical structure theory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9022 (pp. 80-91).

    Embedding Semantic Relations into Word Representations. (Conference Paper)

    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

    Interest Prediction via Users' Actions on Social Media (Journal article)

    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

    Joint Word Representation Learning Using a Corpus and a Semantic Lexicon (Journal article)

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

    Learning Word Representations from Relational Graphs (Conference Paper)

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

    Simultaneous Higher-order Relation Prediction via Collective Incidence Matrix Embedding (Journal article)

    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

    Unsupervised Cross-Domain Word Representation Learning (Conference Paper)

    Bollegala, D., Maehara, T., & Kawarabayashi, K. -I. (2015). Unsupervised Cross-Domain Word Representation Learning. In PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1 (pp. 730-740). Retrieved from http://gateway.webofknowledge.com/

    Unsupervised Cross-Domain Word Representation Learning. (Journal article)

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

    2014

    A Dimension Reduction Approach to Multinomial Relation Prediction (Journal article)

    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

    Learning Word Representations from Relational Graphs. (Journal article)

    Bollegala, D., Maehara, T., Yoshida, Y., & Kawarabayashi, K. -I. (2014). Learning Word Representations from Relational Graphs.. CoRR, abs/1412.2378.

    Learning to Predict Distributions of Words Across Domains (Conference Paper)

    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 http://gateway.webofknowledge.com/

    2013

    Cross-Domain Sentiment Classification using a Sentiment Sensitive Thesaurus (Journal article)

    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. Retrieved from http://www.computer.org/csdl/trans/tk/2013/08/ttk2013081719-abs.html

    Metaphor Interpretation using Paraphrases Extracted from the Web (Journal article)

    Bollegala, D., & Shutova, E. (2013). Metaphor Interpretation using Paraphrases Extracted from the Web. PLoS One, 8(9), e74304. Retrieved from http://www.plosone.org/