Dawei Song


2020

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A Multi-task Learning Framework for Opinion Triplet Extraction
Chen Zhang | Qiuchi Li | Dawei Song | Benyou Wang
Findings of the Association for Computational Linguistics: EMNLP 2020

The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.

2019

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Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
Chen Zhang | Qiuchi Li | Dawei Song
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.

2018

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Quantum-Inspired Complex Word Embedding
Qiuchi Li | Sagar Uprety | Benyou Wang | Dawei Song
Proceedings of The Third Workshop on Representation Learning for NLP

A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words. For example, existing approaches in word embeddings will assign high probabilities to the words “Penguin” and “Fly” if they frequently co-occur, but it fails to capture the fact that they occur in an opposite sense - Penguins do not fly. We hypothesize that humans do not associate a single polarity or sentiment to each word. The word contributes to the overall polarity of a combination of words depending upon which other words it is combined with. This is analogous to the behavior of microscopic particles which exist in all possible states at the same time and interfere with each other to give rise to new states depending upon their relative phases. We make use of the Hilbert Space representation of such particles in Quantum Mechanics where we subscribe a relative phase to each word, which is a complex number, and investigate two such quantum inspired models to derive the meaning of a combination of words. The proposed models achieve better performances than state-of-the-art non-quantum models on binary sentence classification tasks.

2015

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Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification
Ning Xing | Yuexian Hou | Peng Zhang | Wenjie Li | Dawei Song
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2010

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Event-Based Hyperspace Analogue to Language for Query Expansion
Tingxu Yan | Tamsin Maxwell | Dawei Song | Yuexian Hou | Peng Zhang
Proceedings of the ACL 2010 Conference Short Papers