Search Results for author: Yuanhe Tian

Found 15 papers, 13 papers with code

Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks

1 code implementation ACL 2021 Yuanhe Tian, Guimin Chen, Yan Song, Xiang Wan

Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities.

Relation Classification

Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble

1 code implementation NAACL 2021 Yuanhe Tian, Guimin Chen, Yan Song

It is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic guidance for analyzing context and aspect words.

Aspect-Based Sentiment Analysis

Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks

1 code implementation COLING 2020 Guimin Chen, Yuanhe Tian, Yan Song

End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms.

Aspect Extraction

Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams

1 code implementation COLING 2020 Yuanhe Tian, Yan Song, Fei Xia

However, their work on modeling such contextual features is limited to concatenating the features or their embeddings directly with the input embeddings without distinguishing whether the contextual features are important for the joint task in the specific context.

Chinese Word Segmentation Part-Of-Speech Tagging +1

Summarizing Medical Conversations via Identifying Important Utterances

1 code implementation COLING 2020 Yan Song, Yuanhe Tian, Nan Wang, Fei Xia

For the particular dataset used in this study, we show that high-quality summaries can be generated by extracting two types of utterances, namely, problem statements and treatment recommendations.

Named Entity Recognition for Social Media Texts with Semantic Augmentation

1 code implementation EMNLP 2020 Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, Bo Dai

In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.

Chinese Named Entity Recognition NER +1

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

1 code implementation Findings of the Association for Computational Linguistics 2020 Yuyang Nie, Yuanhe Tian, Yan Song, Xiang Ao, Xiang Wan

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text.

Chinese Named Entity Recognition NER

Improving Constituency Parsing with Span Attention

1 code implementation Findings of the Association for Computational Linguistics 2020 Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang

Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task.

Constituency Parsing Natural Language Understanding

Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

1 code implementation EMNLP 2020 Yuanhe Tian, Yan Song, Fei Xia

Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly.

CCG Supertagging

Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge

1 code implementation ACL 2020 Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, Yonggang Wang

Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks.

Chinese Word Segmentation Part-Of-Speech Tagging +1

WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference

1 code implementation WS 2019 Zhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian, Fei Xia

Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings.

Natural Language Inference

ChiMed: A Chinese Medical Corpus for Question Answering

1 code implementation WS 2019 Yuanhe Tian, Weicheng Ma, Fei Xia, Yan Song

Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains.

Question Answering

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