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Cambridge Language Sciences

Interdisciplinary Research Centre
 
Read more at: Mila Marcheva

Mila Marcheva

Bilingualism; (First) language acquisition; Psycholinguistics; Computational modeling of linguistic phenomena


Read more at: Dr Luca Benedetto

Dr Luca Benedetto

Automated evaluation of content for language learners; Modelling of language learners; Natural Language Understanding


Read more at: Georgi Karadzhov

Georgi Karadzhov

Natural language processing, dialogue systems 


Read more at: Christopher Davis

Christopher Davis

Computational modelling of first/second language acquisition,
machine learning,
multimodal semantics


Read more at: Chris Bryant

Chris Bryant

Grammatical error detection and correction, CALL, NLP

Conference proceedings

2017

  • Bryant, CJ., Felice, M. and Briscoe, E., 2017. Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, v. 1
  • Journal articles

    2016

  • Felice, M., Bryant, C. and Briscoe, T., 2016. Automatic extraction of learner errors in ESL sentences using linguistically enhanced alignments COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers,

  • Read more at: Dr Simon Baker

    Dr Simon Baker

    Natural language processing; biomedical text; lexical acquisition

    Journal articles

    2023 (Accepted for publication)

  • Collins, C., Baker, S., Brown, J., Zeng, H., Chan, A., Stenius, U., Narita, M. and Korhonen, A., 2023 (Accepted for publication). Text Mining for Contexts and Relationships in Cancer Genomics Literature Bioinformatics,
    Doi: http://doi.org/10.1093/bioinformatics/btae021
  • 2021

  • Ali, I., Dreij, K., Baker, S., Högberg, J., Korhonen, A. and Stenius, U., 2021. Application of Text Mining in Risk Assessment of Chemical Mixtures: A Case Study of Polycyclic Aromatic Hydrocarbons (PAHs). Environ Health Perspect, v. 129
    Doi: http://doi.org/10.1289/EHP6702
  • Su, Y., Wang, Y., Cai, D., Baker, S., Korhonen, A. and Collier, N., 2021. PROTOTYPE-TO-STYLE: Dialogue Generation with Style-Aware Editing on Retrieval Memory IEEE/ACM Transactions on Audio Speech and Language Processing, v. 29
    Doi: http://doi.org/10.1109/TASLP.2021.3087948
  • Majewska, O., Collins, C., Baker, S., Björne, J., Brown, SW., Korhonen, A. and Palmer, M., 2021. BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. J Biomed Semantics, v. 12
    Doi: http://doi.org/10.1186/s13326-021-00247-z
  • 2020 (Accepted for publication)

  • Vulic, I., Baker, S., Ponti, E., Petti, U., Leviant, I., Wing, K., Majewska, O., Bar, E., Malone, M., Poibeau, T., Reichart, R. and Korhonen, A., 2020 (Accepted for publication). Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity Computational Linguistics,
    Doi: http://doi.org/10.1162/coli_a_00391
  • 2020

  • Wichmann, P., Brintrup, A., Baker, S., Woodall, P. and McFarlane, D., 2020. Extracting supply chain maps from news articles using deep neural networks International Journal of Production Research, v. 58
    Doi: http://doi.org/10.1080/00207543.2020.1720925
  • Crichton, G., Baker, S., Guo, Y. and Korhonen, A., 2020. Neural networks for open and closed Literature-based Discovery. PLoS One, v. 15
    Doi: http://doi.org/10.1371/journal.pone.0232891
  • Petti, U., Baker, S. and Korhonen, A., 2020. A systematic literature review of automatic Alzheimer's disease detection from speech and language. J Am Med Inform Assoc, v. 27
    Doi: http://doi.org/10.1093/jamia/ocaa174
  • Chiu, B. and Baker, S., 2020. Word embeddings for biomedical natural language processing: A survey Language and Linguistics Compass, v. 14
    Doi: http://doi.org/10.1111/lnc3.12402
  • 2019

  • Pyysalo, S., Baker, S., Ali, I., Haselwimmer, S., Shah, T., Young, A., Guo, Y., Högberg, J., Stenius, U., Narita, M. and Korhonen, A., 2019. LION LBD: a literature-based discovery system for cancer biology. Bioinformatics, v. 35
    Doi: http://doi.org/10.1093/bioinformatics/bty845
  • 2018

  • Wichmann, P., Brintrup, A., Baker, S., Woodall, P. and McFarlane, D., 2018. Towards automatically generating supply chain maps from natural language text
    Doi: http://doi.org/10.1016/j.ifacol.2018.08.207
  • 2017 (Accepted for publication)

  • Baker, S., Ali, I., Silins, I., Pyysalo, S., Guo, Y., Högberg, J., Stenius, U. and Korhonen, A., 2017 (Accepted for publication). Cancer Hallmarks Analytics Tool (CHAT): A text mining approach to organise and evaluate scientific literature on cancer Bioinformatics, v. 33
    Doi: http://doi.org/10.1093/bioinformatics/btx454
  • 2017

  • Larsson, K., Baker, S., Silins, I., Guo, Y., Stenius, U., Korhonen, A. and Berglund, M., 2017. Text mining for improved exposure assessment PLOS One, v. 12
    Doi: http://doi.org/10.1371/journal.pone.0173132
  • 2016

  • Baker, S., Silins, I., Guo, Y., Ali, I., Högberg, J., Stenius, U. and Korhonen, A., 2016. Automatic semantic classification of scientific literature according to the hallmarks of cancer. Bioinformatics, v. 32
    Doi: http://doi.org/10.1093/bioinformatics/btv585
  • 2015

  • Korhonen, A., Baker, S., Silins, I., Guo, Y., Ali, I., Hogberg, J. and Stenius, U., 2015. Automatic Semantic Classification of Scientific Literature According to the Hallmarks of Cancer Bioinformatics,
  • Conference proceedings

    2021

  • Su, Y., Cai, D., Wang, Y., Vandyke, D., Baker, S., Li, P. and Collier, N., 2021. Non-Autoregressive Text Generation with Pre-trained Language Models Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics,
  • 2019

  • Chiu, B., Baker, S., Palmer, M. and Korhonen, A., 2019. Enhancing biomedical word embeddings by retrofitting to verb clusters BioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task,
  • 2018

  • Mendes, E., Rodriguez, P., Freitas, V., Baker, S. and Atoui, MA., 2018. Towards improving decision making and estimating the value of decisions in value-based software engineering: the VALUE framework Software Quality Journal, v. 26
    Doi: http://doi.org/10.1007/s11219-017-9360-z
  • Stathopoulos, YA., Baker, S., Rei, M. and Teufel, S., 2018. Variable typing: Assigning meaning to variables in mathematical text NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, v. 1
  • 2017 (No publication date)

  • Baker, S., Korhonen, A. and Pyysalo, S., 2017 (No publication date). Cancer Hallmark Text Classification Using Convolutional Neural Networks
    Doi: http://doi.org/10.17863/CAM.12420
  • 2017

  • Baker, S. and Korhonen, A., 2017. Initializing neural networks for hierarchical multi-label text classification BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop,
  • 2016

  • Baker, S., Kiela, D. and Korhonen, A., 2016. Robust text classification for sparsely labelled data using multi-level embeddings COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers,
  • 2015

  • Korhonen, A., Guo, Y., Baker, S., Yetisgen-Yildiz, M., Stenius, U., Narita, M. and Liò, P., 2015. Improving literature-based discovery with advanced text mining Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8623
    Doi: http://doi.org/10.1007/978-3-319-24462-4_8
  • 2014

  • Baker, S., Reichart, R. and Korhonen, A., 2014. An unsupervised model for instance level subcategorization acquisition EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference,
  • 2010

  • Baker, S. and Mendes, E., 2010. Aggregating Expert-Driven Causal Maps for Web Effort Estimation ADVANCES IN SOFTWARE ENGINEERING, v. 117
  • Baker, S. and Mendes, E., 2010. Evaluating the Weighted Sum Algorithm for Estimating Conditional Probabilities in Bayesian Networks 22ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING & KNOWLEDGE ENGINEERING (SEKE 2010),
  • 2008

  • Baker, S., Au, F., Dobbie, G. and Warren, I., 2008. Automated usability testing using HUI Analyzer ASWEC 2008: 19TH AUSTRALIAN SOFTWARE ENGINEERING CONFERENCE, PROCEEDINGS,
    Doi: http://doi.org/10.1109/ASWEC.2008.40
  • Baker, S., Au, F., Dobbie, G. and Warren, I., 2008. Automated usability testing using HUI analyzer Proceedings of the Australian Software Engineering Conference, ASWEC,
    Doi: http://doi.org/10.1109/ASWEC.2008.4483248

  • Read more at: Olesya Razuvayevskaya

    Olesya Razuvayevskaya

    Natural Language Processing; machine learning; argument mining

    Theses / dissertations

    2022 (No publication date)

  • Razuvayevskaya, O., 2022 (No publication date). Towards automatic interpretation of A Fortiori arguments
    Doi: http://doi.org/10.17863/CAM.86256
  • Conference proceedings

    2017

  • Razuvayevskaya, O. and Teufel, SH., 2017. Recognising enthymemes in real-world texts: A feasibility study
    Doi: http://doi.org/10.17863/CAM.12376
  • Journal articles

    2017

  • Razuvayevskaya, O. and Teufel, S., 2017. Finding enthymemes in real-world texts: A feasibility study Argument & Computation, v. 8
    Doi: http://doi.org/10.3233/AAC-170020

  • Read more at: Dr Marek Rei

    Dr Marek Rei

    Machine learning;
    neural network models;
    sequence labeling tasks;
    automated assessment

    Conference proceedings

    2019

  • Rei, M. and Sogaard, A., 2019. Jointly Learning to Label Sentences and Tokens THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE,
  • 2018 (Accepted for publication)

  • Rei, M. and Søgaard, A., 2018 (Accepted for publication). Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens
    Doi: http://doi.org/10.17863/CAM.35110
  • 2018

  • Rei, M., Gerz, D. and Vulić, I., 2018. Scoring lexical entailment with a supervised directional similarity network ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), v. 2
    Doi: http://doi.org/10.18653/v1/p18-2101
  • Stathopoulos, YA., Baker, S., Rei, M. and Teufel, S., 2018. Variable typing: Assigning meaning to variables in mathematical text NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, v. 1
  • Barrett, M., Bingel, J., Hollenstein, N., Rei, M. and Søgaard, A., 2018. Sequence classification with human attention CoNLL 2018 - 22nd Conference on Computational Natural Language Learning, Proceedings,
    Doi: http://doi.org/10.18653/v1/k18-1030
  • 2017 (Accepted for publication)

  • Rei, M., Bulat, LT., Kiela, D. and Shutova, E., 2017 (Accepted for publication). Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
  • 2017

  • Rei, M., 2017. Semi-supervised multitask learning for sequence labeling ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), v. 1
    Doi: http://doi.org/10.18653/v1/P17-1194
  • Rei, M., 2017. Detecting Off-topic Responses to Visual Prompts
  • Rei, M. and Giannakoudaki, E., 2017. Auxiliary Objectives for Neural Error Detection Models
  • Giannakoudaki, E., Rei, M., Andersen, OE. and Yuan, Z., 2017. Neural Sequence-Labelling Models for Grammatical Error Correction Proceedings of the 2017 Conference on Empirical Methods in natural Language Processing, v. D17-1
    Doi: http://doi.org/10.18653/v1/D17-1297
  • Rei, M., Felice, M., Yuan, Z. and Briscoe, T., 2017. Artificial Error Generation with Machine Translation and Syntactic Patterns Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications,
  • Farag, Y., Rei, M. and Briscoe, T., 2017. An Error-Oriented Approach to Word Embedding Pre-Training Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications,
  • 2016 (Accepted for publication)

  • Rei, M. and Cao, K., 2016 (Accepted for publication). A Joint Model for Word Embedding and Word Morphology
  • 2016

  • Rei, M., Crichton, GKO. and Pyysalo, S., 2016. Attending to characters in neural sequence labeling models COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers,
  • Alikaniotis, D., Yannakoudakis, H. and Rei, M., 2016. Automatic text scoring using neural networks 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers, v. 2
    Doi: http://doi.org/10.18653/v1/p16-1068
  • Rei, M. and Yannakoudakis, H., 2016. Compositional sequence labeling models for error detection in learner writing 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers, v. 2
  • Rei, M. and Cummins, R., 2016. Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays https://aclweb.org/anthology/volumes/proceedings-of-the-11th-workshop-on-innovative-use-of-nlp-for-building-educational-applications/,
    Doi: http://doi.org/10.18653/v1/W16-05
  • 2015

  • Rei, M., 2015. Online representation learning in recurrent neural language models Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing,
  • 2014

  • Rei, M. and Briscoe, T., 2014. Parser lexicalisation through self-learning NAACL HLT 2013 - 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Main Conference,
  • Rei, M. and Briscoe, T., 2014. Looking for Hyponyms in Vector Space. CoNLL,
  • 2011

  • Rei, M. and Briscoe, T., 2011. Unsupervised Entailment Detection between Dependency Graph Fragments
    Doi: http://doi.org/10.17863/CAM.21358
  • 2010

  • Rei, M. and Briscoe, T., 2010. Combining manual rules and supervised learning for hedge cue and scope detection Proceedings of the Fourteenth Conference on Computational Natural Language Learning: Shared Task,
  • Journal articles

    2017

  • Farag, Y., Rei, M. and Briscoe, T., 2017. An Error-Oriented Approach to Word Embedding Pre-Training. CoRR, v. abs/1707.06841
  • Rei, M., Felice, M., Yuan, Z. and Briscoe, T., 2017. Artificial Error Generation with Machine Translation and Syntactic Patterns. CoRR, v. abs/1707.05236
  • 2011

  • Briscoe, T., Harrison, K., Naish, A., Parker, A., Rei, M., Siddharthan, A., Sinclair, D., Slater, M. and Watson, R., 2011. Intelligent Information Access from Scientific Papers CURRENT CHALLENGES IN PATENT INFORMATION RETRIEVAL, v. 29
    Doi: 10.1007/978-3-642-19231-9_16
  • Theses / dissertations

    2013

  • Rei, M., 2013. Minimally supervised dependency-based methods for natural language processing

  • Read more at: Dr Øistein E. Andersen

    Dr Øistein E. Andersen

     

    Journal articles

    2018

  • Yannakoudakis, H., Andersen, ØE., Geranpayeh, A., Briscoe, T. and Nicholls, D., 2018. Developing an automated writing placement system for ESL learners Applied Measurement in Education, v. 31
    Doi: http://doi.org/10.1080/08957347.2018.1464447
  • Conference proceedings

    2017

  • Giannakoudaki, E., Rei, M., Andersen, OE. and Yuan, Z., 2017. Neural Sequence-Labelling Models for Grammatical Error Correction Proceedings of the 2017 Conference on Empirical Methods in natural Language Processing, v. D17-1
    Doi: http://doi.org/10.18653/v1/D17-1297
  • 2012

  • Kochmar, E., Andersen, O. and Briscoe, E., 2012. HOO 2012 Error Recognition and Correction Shared Task: Cambridge University Submission Report http://aclweb.org/anthology/W12-2028, v. Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

  • Read more at: Mariano Felice

    Mariano Felice

    Grammatical error detection and correction in non-native English text


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    Cambridge Language Sciences is an Interdisciplinary Research Centre at the University of Cambridge. Our virtual network connects researchers from five schools across the university as well as other world-leading research institutions. Our aim is to strengthen research collaborations and knowledge transfer across disciplines in order to address large-scale multi-disciplinary research challenges relating to language research.

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