Titlebook: Natural Language Processing and Chinese Computing; 7th CCF Internationa Min Zhang,Vincent Ng,Hongying Zan Conference proceedings 2018 Sprin

無可爭辯

手銬

Neuropeptides

: A Bi-Channel Tree Convolution Based Neural Network Model for Relation Classificationworks. This paper proposes a bi-channel tree convolution based neural network model, ., which combines syntactic tree features and other lexical level features together in a deeper manner for relation classification. First, each input sentence is parsed into a syntactic tree. Then, this tree is deco

Endometrium

Using Entity Relation to Improve Event Detection via Attention Mechanismal networks have successfully solve the problem to some extent, by encoding a series of linguistic features, such as lexicon, part-of-speech and entity. However, so far, the entity relation hasn’t yet been taken into consideration. In this paper, we propose a novel event extraction method to exploit

使服水土

Bernstein-test

Learning BLSTM-CRF with Multi-channel Attribute Embedding for Medical Information ExtractionE) is an essential step in it. This paper focuses on the medical IE, whose aim is to extract the pivotal contents from the medical texts such as drugs, treatments and so on. In existing works, introducing side information into neural network based Conditional Random Fields (CRFs) models have been ve

系列

極小量

I Know There Is No Answer: Modeling Answer Validation for Machine Reading Comprehensionevaluate these methods, we build a dataset SQuAD-T based on the SQuAD dataset, which consists of questions in the SQuAD dataset and includes relevant passages that may not contain the answer. We report results on this dataset and provides comparisons and analysis of the different models.

faction

Using Entity Relation to Improve Event Detection via Attention Mechanismmatically investigate the effect of relation representation between entities. In addition, we also use different attention strategies in the model. Experimental results show that our approach outperforms other state-of-the-art methods.

shrill

From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generationcement learning (PGRL). We conduct experiments on the recently-introduced ROCStories Corpus. We evaluate our model in both automatic evaluation and human evaluation. Experimental results show that our model exceeds the sequence-to-sequence baseline model by 15.75% and 13.57% in terms of CIDEr and consistency score respectively.