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Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training ...
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Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network ...
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N-LTP: An Open-source Neural Language Technology Platform for Chinese ...
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Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation ...
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Neural Personalized Response Generation as Domain Adaptation ...
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Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning ...
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Additional file 1: of Prediction of the next highly pathogenic avian influenza pandemic that can cause illness in humans ...
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Additional file 1: of Prediction of the next highly pathogenic avian influenza pandemic that can cause illness in humans ...
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Effective LSTMs for Target-Dependent Sentiment Classification ...
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Abstract:
Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons. ... : 7 pages, 3 figures published in COLING 2016 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1512.01100 https://arxiv.org/abs/1512.01100
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