skip to content

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

  • 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
  • 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
  • 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., 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,
  • 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
  • 2016 (Accepted for publication)

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

  • 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
  • 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,
  • 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


    What we do

    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.

    JOIN OUR NETWORK

    JOIN OUR MAILING LIST

    CONTACT US