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Cambridge Language Sciences

Interdisciplinary Research Centre
 

Read more at: David Strohmaier

David Strohmaier

I am David Strohmaier, a computer scientist and philosopher. Currently, I work as a research associate in the Natural Language and Information Processing (NLIP) group at the University of Cambridge.

My research applies machine/deep learning to lexical semantic acquisition. How do neural models learn the meaning of words and to what extent does that reflect our own learning processes?


Read more at: Paul Siewert

Paul Siewert

[pʰaʊ̯l ˈziː.vɛɐ̯tʰ]

I am a PhD student at Jesus College. My training is mathematical, but the main work I do with my supervisor Fermín Moscoso del Prado Martín is in mathematical linguistics. Please see my page on the Computer lab website for my non-linguistic interests.

"What counts is not what you cover, but what you uncover!"


Read more at: Suchir Salhan

Suchir Salhan

Suchir Salhan is a PhD Candidate in the Department of Computer Science & Technology at the University of Cambridge (Gonville & Caius College) researching Small Language Models and Cognitively-Inspired AI. He previously completed a BA and MEng in Computer Science & Linguistics at Gonville & Caius College, obtaining a “starred First” (Class I with Distinction) and a Distinction respectively.


Read more at: Fermin Moscoso del Prado Martin

Fermin Moscoso del Prado Martin


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 

Theses / dissertations

2024 (No publication date)

  • Karadzhov, G., 2024 (No publication date). DEliBots : Deliberation Enhancing Bots
    Doi: http://doi.org/10.17863/CAM.109182
  • Journal articles

    2023

  • Karadzhov, G., Stafford, T. and Vlachos, A., 2023. DeliData: A Dataset for Deliberation in Multi-party Problem Solving Proceedings of the ACM on Human-Computer Interaction, v. 7
    Doi: http://doi.org/10.1145/3610056

  • Read more at: Christopher Davis

    Christopher Davis

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

    Theses / dissertations

    2024 (No publication date)

  • Davis, C., 2024 (No publication date). On the evaluation and application of neural language models for grammatical error detection
    Doi: http://doi.org/10.17863/CAM.108291
  • Conference proceedings

    2023

  • Caines, A., Benedetto, L., Taslimipoor, S., Davis, C., Gao, Y., Andersen, Ø., Yuan, Z., Elliott, M., Moore, R., Bryant, C., Rei, M., Yannakoudakis, H., Mullooly, A., Nicholls, D. and Buttery, P., 2023. On the application of Large Language Models for language teaching and assessment technology CEUR Workshop Proceedings, v. 3487
  • Diehl Martinez, R., Goriely, Z., McGovern, H., Davis, C., Caines, A., Buttery, P. and Beinborn, L., 2023. CLIMB – Curriculum Learning for Infant-inspired Model Building CoNLL 2023 - BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, Proceedings,
  • 2022

  • Davis, C., Bryant, C., Caines, A., Rei, M. and Buttery, P., 2022. Probing for targeted syntactic knowledge through grammatical error detection CoNLL 2022 - 26th Conference on Computational Natural Language Learning, Proceedings of the Conference,
  • 2021

  • Yuan, Z., Taslimipoor, S., Davis, C. and Bryant, C., 2021. Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings,
    Doi: 10.18653/v1/2021.emnlp-main.687
  • 2019

  • Zaidi, AH., Caines, A., Davis, C., Moore, R., Buttery, P. and Rice, A., 2019. Accurate modelling of language learning tasks and students using representations of grammatical proficiency EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining,

  • Read more at: Chris Bryant

    Chris Bryant

    Grammatical error detection and correction, CALL, NLP

    Theses / dissertations

    2019

  • Bryant, CJ., 2019. Automatic annotation of error types for grammatical error correction
    Doi: http://doi.org/10.17863/CAM.40832
  • 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,

  • 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.

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