Jiangming Liu

Jinan Xu, Jiangming Liu, Yujie Zhang
J. Inf. Sci. Eng.
2015

Word similarity computing is one of the most important and fundamental task in the field of natural language processing. Most of word similarity methods perform well in synonyms, but not well between… (More)

Shift-Reduce Constituent Parsing with Neural Lookahead Features

Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which… (More)

Attention Modeling for Targeted Sentiment

Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for… (More)

Encoder-Decoder Shift-Reduce Syntactic Parsing

Starting from NMT, encoder-decoder neural networks have been used for many NLP problems. Graph-based models and transition-based models borrowing the encoder components achieve state-of-the-art… (More)

An Empirical Comparison Between N-gram and Syntactic Language Models for Word Ordering

Syntactic language models and N-gram language models have both been used in word ordering. In this paper, we give an empirical comparison between N-gram and syntactic language models on word order… (More)

In-Order Transition-based Constituent Parsing

Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the… (More)

A Hybrid Topic Model for Multi-Document Summarization

Jin An Xu, Jiangming Liu, Kenji Araki
IEICE Transactions
2015

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text… (More)

An Approach of Hybrid Hierarchical Structure for Word Similarity Computing by HowNet

Word similarity computing is an important and fundamental task in the field of natural language processing. Most of word similarity methods perform well in synonyms, but not well between words whose… (More)

Discourse Representation Structure Parsing

Mirella Lapata, Shay B. Cohen, Jiangming Liu
ACL
2018

We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which… (More)

Learning Domain Representation for Multi-Domain Sentiment Classification

Qi Liu, Yue Zhang, Jiangming Liu
NAACL-HLT
2018

Training data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on… (More)

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