论文信息 - SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification

SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification

This paper describes our system, SciREL (Scientific abstract RELation extraction system), developed for the SemEval 2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers. We present a feature-vector based system to extract explicit semantic relation and classify them. Our system is trained in the ACL corpus (BIrd et al., 2008) that contains annotated abstracts given by the task organizers. When an abstract with annotated entities is given as the input into our system, it extracts the semantic relations through a set of defined features and classifies them into one of the given six categories of relations through feature engineering and a learned model. For the best combination of features, our system SciREL obtained an F-measure of 20.03 on the official test corpus which includes 150 abstracts in the relation classification Subtask 1.1. In this paper, we provide an in-depth error analysis of our results to prevent duplication of research efforts in the development of future systems

Bridget T. McInnes | Chathurika S. Wickramasinghe | Darshini Mahendran | Chathurika Brahmana | D. Mahendran

[1] Peter Szolovits,et al. Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations , 2017, Briefings Bioinform..

[2] Natalia Konstantinova,et al. Review of Relation Extraction Methods: What Is New Out There? , 2014, AIST.

[3] Parisa Kordjamshidi,et al. EDISON: Feature Extraction for NLP, Simplified , 2016, LREC.

[4] Isabelle Augenstein,et al. SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications , 2017, *SEMEVAL.

[5] Jian Su,et al. Exploring Various Knowledge in Relation Extraction , 2005, ACL.

[6] Behrang Q. Zadeh,et al. SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers , 2018, *SEMEVAL.

[7] W. Alkema,et al. Application of text mining in the biomedical domain. , 2015, Methods.

[8] Yifan Peng,et al. Improving chemical disease relation extraction with rich features and weakly labeled data , 2016, Journal of Cheminformatics.

[9] KüffnerRobert,et al. RelEx---Relation extraction using dependency parse trees , 2007 .

[10] Dursun Delen,et al. Seeding the survey and analysis of research literature with text mining , 2008, Expert Syst. Appl..

[11] Dragomir R. Radev,et al. The ACL Anthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics , 2008, LREC.