MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 956–960, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics MayoNLP at SemEval 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications Sijia Liu1,2, Feichen Shen1, Vipin Chaudhary2, and Hongfang Liu1 1Department of Health Science Research, Mayo Clinic, USA {liu.sijia,shen.feichen,liu.hongfang}@mayo.edu 2Department of Computer Science and Engineering, SUNY at Buffalo, USA vipin@buffalo.edu Abstract In this paper, we present MayoNLP’s results from the participation in the Sci- enceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models. Word Embed- ding Distance Pattern, which uses the head noun word embedding to generate distance patterns based on labeled keyphrases, is proposed as an incremental feature set to enhance the conventional Named Entity Recognition feature sets. Support vector machine is used as the supervised classifier for keyphrase classification. Our system achieved an overall F1 score of 0.67 for keyphrase classification and 0.64 for keyphrase classification and relation detection. 1 Introduction In this paper, we present details of our partic- ipation in the SemEval 2017 Task 10, Scien- ceIE (Augenstein et al., 2017). Named Entity Recognition (NER) is one of the major challenges in Natural Language Processing (NLP) and text mining. The interesting entity types in NER tasks vary from communities and corpora. In general, NLP community mainly focused on the identification of proper nouns or noun phrases, e.g., locations, names and organizations in news corpora (Nadeau and Sekine, 2007). In contrast, biomedical community is more interested in find- ing biomedical or clinical terminologies (Leaman and Gonzalez, 2008; Tsuruoka and Tsujii, 2005) in biomedical texts and scientific literatures. There are several machine learning based methods used in biomedical NER, which include Support Vector Machine (SVM) (Lee et al., 2004), Hidden Markov Model (HMM) (Zhou and Su, 2004) and Conditional Random Field (CRF) (Tsai et al., 2006). Semantic word embedding (Mikolov et al., 2013) is designed to capture different degrees of similarity between words using a vectorized representation, which preserves semantic and syntactic relationships. Word embeddings and word embedding based features have drawn increasing attention for classification tasks (Ma et al., 2015) and similarity prediction tasks (Afzal et al., 2016). We leveraged pre-trained word embeddings to obtain head noun pattern features, and combined several other NER feature sets to improve the keyphrase classification performance. Although our team participated in Scenario 2 (keyphrase classification and relation detection), our efforts were focused on keyphrase classification task (Subtask B). For the relation detection problem (Subtask C), we implemented a straightforward rule-based system to detect synonyms and hy- ponyms given annotated keyphrases. The rest of the paper is organized as follows. Section 2 briefly introduces the corpus used in this task. Section 3 discusses the methods proposed in our NER system. Section 4 addresses the experimental results in the development set, our submitted runs and official evaluation results. Finally, Section 5 concludes …

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