Lana Yeganova


2023

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Findings of the WMT 2023 Biomedical Translation Shared Task: Evaluation of ChatGPT 3.5 as a Comparison System
Mariana Neves | Antonio Jimeno Yepes | Aurélie Névéol | Rachel Bawden | Giorgio Maria Di Nunzio | Roland Roller | Philippe Thomas | Federica Vezzani | Maika Vicente Navarro | Lana Yeganova | Dina Wiemann | Cristian Grozea
Proceedings of the Eighth Conference on Machine Translation

We present an overview of the Biomedical Translation Task that was part of the Eighth Conference on Machine Translation (WMT23). The aim of the task was the automatic translation of biomedical abstracts from the PubMed database. It included twelve language directions, namely, French, Spanish, Portuguese, Italian, German, and Russian, from and into English. We received submissions from 18 systems and for all the test sets that we released. Our comparison system was based on ChatGPT 3.5 and performed very well in comparison to many of the submissions.

2022

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Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports
Mariana Neves | Antonio Jimeno Yepes | Amy Siu | Roland Roller | Philippe Thomas | Maika Vicente Navarro | Lana Yeganova | Dina Wiemann | Giorgio Maria Di Nunzio | Federica Vezzani | Christel Gerardin | Rachel Bawden | Darryl Johan Estrada | Salvador Lima-lopez | Eulalia Farre-maduel | Martin Krallinger | Cristian Grozea | Aurelie Neveol
Proceedings of the Seventh Conference on Machine Translation (WMT)

In the seventh edition of the WMT Biomedical Task, we addressed a total of seven languagepairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian. This year’s test sets covered three types of biomedical text genre. In addition to scientific abstracts and terminology items used in previous editions, we released test sets of clinical cases. The evaluation of clinical cases translations were given special attention by involving clinicians in the preparation of reference translations and manual evaluation. For the main MEDLINE test sets, we received a total of 609 submissions from 37 teams. For the ClinSpEn sub-task, we had the participation of five teams.

2021

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Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set
Lana Yeganova | Dina Wiemann | Mariana Neves | Federica Vezzani | Amy Siu | Inigo Jauregi Unanue | Maite Oronoz | Nancy Mah | Aurélie Névéol | David Martinez | Rachel Bawden | Giorgio Maria Di Nunzio | Roland Roller | Philippe Thomas | Cristian Grozea | Olatz Perez-de-Viñaspre | Maika Vicente Navarro | Antonio Jimeno Yepes
Proceedings of the Sixth Conference on Machine Translation

In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque. Further, our tests were composed of three types of textual test sets. New to this year, we released a test set of summaries of animal experiments, in addition to the test sets of scientific abstracts and terminologies. We received a total of 107 submissions from 15 teams from 6 countries.

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Measuring the relative importance of full text sections for information retrieval from scientific literature.
Lana Yeganova | Won Gyu Kim | Donald Comeau | W John Wilbur | Zhiyong Lu
Proceedings of the 20th Workshop on Biomedical Language Processing

With the growing availability of full-text articles, integrating abstracts and full texts of documents into a unified representation is essential for comprehensive search of scientific literature. However, previous studies have shown that naïvely merging abstracts with full texts of articles does not consistently yield better performance. Balancing the contribution of query terms appearing in the abstract and in sections of different importance in full text articles remains a challenge both with traditional bag-of-words IR approaches and for neural retrieval methods. In this work we establish the connection between the BM25 score of a query term appearing in a section of a full text document and the probability of that document being clicked or identified as relevant. Probability is computed using Pool Adjacent Violators (PAV), an isotonic regression algorithm, providing a maximum likelihood estimate based on the observed data. Using this probabilistic transformation of BM25 scores we show an improved performance on the PubMed Click dataset developed and presented in this study, as well as the 2007 TREC Genomics collection.

2020

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Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages
Rachel Bawden | Giorgio Maria Di Nunzio | Cristian Grozea | Inigo Jauregi Unanue | Antonio Jimeno Yepes | Nancy Mah | David Martinez | Aurélie Névéol | Mariana Neves | Maite Oronoz | Olatz Perez-de-Viñaspre | Massimo Piccardi | Roland Roller | Amy Siu | Philippe Thomas | Federica Vezzani | Maika Vicente Navarro | Dina Wiemann | Lana Yeganova
Proceedings of the Fifth Conference on Machine Translation

Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.

2018

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SingleCite: Towards an improved Single Citation Search in PubMed
Lana Yeganova | Donald C Comeau | Won Kim | W John Wilbur | Zhiyong Lu
Proceedings of the BioNLP 2018 workshop

A search that is targeted at finding a specific document in databases is called a Single Citation search. Single citation searches are particularly important for scholarly databases, such as PubMed, because users are frequently searching for a specific publication. In this work we describe SingleCite, a single citation matching system designed to facilitate user’s search for a specific document. We report on the progress that has been achieved towards building that functionality.

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MeSH-based dataset for measuring the relevance of text retrieval
Won Gyu Kim | Lana Yeganova | Donald Comeau | W John Wilbur | Zhiyong Lu
Proceedings of the BioNLP 2018 workshop

Creating simulated search environments has been of a significant interest in infor-mation retrieval, in both general and bio-medical search domains. Existing collec-tions include modest number of queries and are constructed by manually evaluat-ing retrieval results. In this work we pro-pose leveraging MeSH term assignments for creating synthetic test beds. We select a suitable subset of MeSH terms as queries, and utilize MeSH term assignments as pseudo-relevance rankings for retrieval evaluation. Using well studied retrieval functions, we show that their performance on the proposed data is consistent with similar findings in previous work. We further use the proposed retrieval evaluation framework to better understand how to combine heterogeneous sources of textual information.

2016

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PubTermVariants: biomedical term variants and their use for PubMed search
Lana Yeganova | Won Kim | Sun Kim | Rezarta Islamaj Doğan | Wanli Liu | Donald C Comeau | Zhiyong Lu | W John Wilbur
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

2015

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Summarizing Topical Contents from PubMed Documents Using a Thematic Analysis
Sun Kim | Lana Yeganova | W. John Wilbur
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2011

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Text Mining Techniques for Leveraging Positively Labeled Data
Lana Yeganova | Donald C. Comeau | Won Kim | W. John Wilbur
Proceedings of BioNLP 2011 Workshop