Search · four archives
Search · four archives
17 papers · ranked by Valyu relevance
Zhongyang Fang, Yue Cong, Yuhan Chai, Chengliang Gao + 3 more
'Jing Qiu' 'Wray Buntine'] Implicit discourse relation recognition (IDRR) has long been considered a challenging problem in shallow discourse parsing. The absence of connectives makes such relations implicit and requires much more effort to understand the semantics of the text. Thus, it is important to preserve the…
Hongxiao Bai, Hai Zhao, Junhan Zhao
Implicit discourse relation recognition is a challenging task due to the absence of the necessary informative clue from explicit connectives. The prediction of relations requires a deep understanding of the semantic meanings of sentence pairs. As implicit discourse relation recognizer has to carefully tackle the…
Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu + 1 more
Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another…
Xinyi Cai
Implicit discourse relation recognition is a challenging task in discourse analysis due to the absence of explicit discourse connectives between spans of text. Recent pre-trained language models have achieved great success on this task. However, there is no fine-grained analysis of the performance of these pre-trained…
Mingyu Derek, Kevin K. Bowden, Jiaqi Wu, Wen Cui + 1 more
Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently…
Wei Shi, Vera Demberg
Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse relational arguments. Therefore, an effective encoding of the relational arguments is of crucial…
Gregor Weiss, Marko Bajec, Fabio Calefato
Understanding the sense of discourse relations between segments of text is essential to truly comprehend any natural language text. Several automated approaches have been suggested, but all rely on external resources, linguistic feature engineering, and their processing pipelines are built from substantially different…
Samuel Rönnqvist, Niko Schenk, Christian Chiarcos
We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the…
Yangfeng Ji, Jacob Eisenstein
Discourse relations bind smaller linguistic elements into coherent texts. However, automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked sentences. A more subtle challenge is that it is not enough to represent the meaning of each sentence of a…
Yizhong Wang, Sujian Li, Jingfeng Yang, Xu Sun + 1 more
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text. To tackle this task, recent studies have tried several deep learning methods but few of them exploited the syntactic information. In this work, we explore the idea of…
Rashmi Prasad, Susan McRoy, Nadya Frid, Aravind Joshi + 1 more
Background Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text corpus annotated with discourse relations would be very useful for developing and evaluating methods for biomedical…
Jennifer D’Souza, Vincent Ng
Motivation: We examine the task of temporal relation classification for the clinical domain. Our approach to this task departs from existing ones in that it is (i) ‘knowledge-rich’, employing sophisticated knowledge derived from discourse relations as well as both domain-independent and domain-dependent semantic…
Mei Yoshikawa, Tadahaya Miuzuno, Yohei Ohto, Hiromi Fujimoto + 1 more
Extracting cell–cell relations from biomedical literature is essential for understanding intercellular communication in immunity, inflammation, and tissue biology. However, cell–cell relation extraction has not been established as a standalone biomedical relation extraction task, and no benchmark corpus or systematic…
Anuj Sharma, Vassilis Virvilis, Tina Lekka, Christos Andronis
The goal of Biomedical relation extraction is to uncover high-quality relations from life science literature with diverse applications in the fields of Biology and Medicine. In the last decade, several methods can be found in published literature ranging from binary to complex relation extraction. In this work, we…
Hasin Rehana, Junguk Hur
Pharmacovigilance relies on accurate extraction of structured biomedical entities and their semantic relationships from scientific literature. However, most biomedical information extraction systems address named entity recognition (NER) and relation extraction as separate tasks trained on corpus-specific…
Angus Roberts, Robert Gaizauskas, Mark Hepple, Yikun Guo
Background The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the…
Peng Su, Gang Li, Cathy Wu, K. Vijay-Shanker
Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature.…