Karin Verspoor


2022

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What does it take to bake a cake The RecipeRef corpus and anaphora resolution in procedural textRecipeRef corpus and anaphora resolution in procedural text
Biaoyan Fang | Timothy Baldwin | Karin Verspoor
Findings of the Association for Computational Linguistics: ACL 2022

Procedural text contains rich anaphoric phenomena yet has not received much attention in NLP To fill this gap we investigate the textual properties of two types of procedural text recipes and chemical patents and generalize an anaphora annotation framework developed for the chemical domain for modeling anaphoric phenomena in recipes We apply this framework to annotate the RecipeRef corpus with both bridging and coreference relations Through comparison to chemical patents we show the complexity of anaphora resolution in recipes We demonstrate empirically that transfer learning from the chemical domain improves resolution of anaphora in recipes suggesting transferability of general procedural knowledge

2021

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ChEMU-Ref : A Corpus for Modeling Anaphora Resolution in the Chemical DomainChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain
Biaoyan Fang | Christian Druckenbrodt | Saber A Akhondi | Jiayuan He | Timothy Baldwin | Karin Verspoor
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.

2020

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WikiUMLS : Aligning UMLS to Wikipedia via Cross-lingual Neural RankingWikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
Afshin Rahimi | Timothy Baldwin | Karin Verspoor
Proceedings of the 28th International Conference on Computational Linguistics

We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources. We propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1of 72 %, a substantial improvement of 20 % over word- and char-level BM25, enabling manual alignment with minimal effort. We release our resources, including ranked Wikipedia pages for 700k UMLSconcepts, and WikiUMLS, a dataset for training and evaluation of alignment models between UMLS and Wikipedia collected from Wikidata. This will provide easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings.

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Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
Karin Verspoor | Kevin Bretonnel Cohen | Mark Dredze | Emilio Ferrara | Jonathan May | Robert Munro | Cecile Paris | Byron Wallace
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

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Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
Karin Verspoor | Kevin Bretonnel Cohen | Michael Conway | Berry de Bruijn | Mark Dredze | Rada Mihalcea | Byron Wallace
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

2019

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Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

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Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

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Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

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Detecting Chemical Reactions in Patents
Hiyori Yoshikawa | Dat Quoc Nguyen | Zenan Zhai | Christian Druckenbrodt | Camilo Thorne | Saber A. Akhondi | Timothy Baldwin | Karin Verspoor
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association

Extracting chemical reactions from patents is a crucial task for chemists working on chemical exploration. In this paper we introduce the novel task of detecting the textual spans that describe or refer to chemical reactions within patents. We formulate this task as a paragraph-level sequence tagging problem, where the system is required to return a sequence of paragraphs which contain a description of a reaction. To address this new task, we construct an annotated dataset from an existing proprietary database of chemical reactions manually extracted from patents. We introduce several baseline methods for the task and evaluate them over our dataset. Through error analysis, we discuss what makes the task complex and challenging, and suggest possible directions for future research.

2018

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Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognitionCNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition
Zenan Zhai | Dat Quoc Nguyen | Karin Verspoor
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks. Empirical results over the BioCreative V CDR corpus show that the use of either type of character-level word embeddings in conjunction with the BiLSTM-CRF models leads to comparable state-of-the-art performance. However, the models using CNN-based character-level word embeddings have a computational performance advantage, increasing training time over word-based models by 25 % while the LSTM-based character-level word embeddings more than double the required training time.

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Proceedings of the Third Conference on Machine Translation: Research Papers
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Lucia Specia | Marco Turchi | Karin Verspoor
Proceedings of the Third Conference on Machine Translation: Research Papers

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Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Lucia Specia | Marco Turchi | Karin Verspoor
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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Findings of the WMT 2018 Biomedical Translation Shared Task : Evaluation on Medline test setsWMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets
Mariana Neves | Antonio Jimeno Yepes | Aurélie Névéol | Cristian Grozea | Amy Siu | Madeleine Kittner | Karin Verspoor
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e.g. research results presented in scientific publications. In this paper, we provide an overview of the Biomedical Translation shared task in the Workshop on Machine Translation (WMT) 2018, which specifically examined the performance of machine translation systems for biomedical texts. This year, we provided test sets of scientific publications from two sources (EDP and Medline) and for six language pairs (English with each of Chinese, French, German, Portuguese, Romanian and Spanish). We describe the development of the various test sets, the submissions that we received and the evaluations that we carried out. We obtained a total of 39 runs from six teams and some of this year’s BLEU scores were somewhat higher that last year’s, especially for teams that made use of biomedical resources or state-of-the-art MT algorithms (e.g. Transformer). Finally, our manual evaluation scored automatic translations higher than the reference translations for German and Spanish.

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An Improved Neural Network Model for Joint POS Tagging and Dependency ParsingPOS Tagging and Dependency Parsing
Dat Quoc Nguyen | Karin Verspoor
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51 % and 92.87 %, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97 %. Furthermore, experimental results on parsing 61 big Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8 % higher average POS tagging score and 3.6 % higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at :https://github.com/datquocnguyen/jPTDP\n

2017

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SemEval-2017 Task 3 : Community Question AnsweringSemEval-2017 Task 3: Community Question Answering
Preslav Nakov | Doris Hoogeveen | Lluís Màrquez | Alessandro Moschitti | Hamdy Mubarak | Timothy Baldwin | Karin Verspoor
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describe SemEval2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016 : (A) QuestionComment Similarity, (B) QuestionQuestion Similarity, (C) QuestionExternal Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks AD. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks AC.