Implicit discourse relation detection using concatenated word embeddings and a gated relevance network
نویسندگان
چکیده
منابع مشابه
Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings
We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition. Specifically, DSWE is learned by performing connective classification on massive explicit discourse data, and capable of capturing discourse relationships between words. On the PDTB data set, using DSWE as features achieves significant improvements over base...
متن کاملImplicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network
Word pairs, which are one of the most easily accessible features between two text segments, have been proven to be very useful for detecting the discourse relations held between text segments. However, because of the data sparsity problem, the performance achieved by using word pair features is limited. In this paper, in order to overcome the data sparsity problem, we propose the use of word em...
متن کاملA Stacking Gated Neural Architecture for Implicit Discourse Relation Classification
Discourse parsing is considered as one of the most challenging natural language processing (NLP) tasks. Implicit discourse relation classification is the bottleneck for discourse parsing. Without the guide of explicit discourse connectives, the relation of sentence pairs are very hard to be inferred. This paper proposes a stacking neural network model to solve the classification problem in whic...
متن کاملImplicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode the latent semantic information, and are suitable for recognizing semantic relations between argument pairs. However, conventional vector representations usually adopt em...
متن کاملLearning Connective-based Word Representations for Implicit Discourse Relation Identification
We introduce a simple semi-supervised approach to improve implicit discourse relation identification. This approach harnesses large amounts of automatically extracted discourse connectives along with their arguments to construct new distributional word representations. Specifically, we represent words in the space of discourse connectives as a way to directly encode their rhetorical function. E...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Science China Information Sciences
سال: 2019
ISSN: 1674-733X,1869-1919
DOI: 10.1007/s11432-018-9528-8