Wee Sun Lee


2019

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An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Ruidan He | Wee Sun Lee | Hwee Tou Ng | Daniel Dahlmeier
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus. In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level. Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.

2018

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Exploiting Document Knowledge for Aspect-level Sentiment Classification
Ruidan He | Wee Sun Lee | Hwee Tou Ng | Daniel Dahlmeier
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document-level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015, and 2016, and we show that attention-based LSTM benefits from document-level knowledge in multiple ways.

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Effective Attention Modeling for Aspect-Level Sentiment Classification
Ruidan He | Wee Sun Lee | Hwee Tou Ng | Daniel Dahlmeier
Proceedings of the 27th International Conference on Computational Linguistics

Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.

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Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
Ruidan He | Wee Sun Lee | Hwee Tou Ng | Daniel Dahlmeier
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations — entropy minimization and self-ensemble bootstrapping — to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.

2017

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An Unsupervised Neural Attention Model for Aspect Extraction
Ruidan He | Wee Sun Lee | Hwee Tou Ng | Daniel Dahlmeier
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discovering coherent aspects. The model improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings. Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space. In addition, we use an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects. Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects, and substantially outperforms baseline methods on several evaluation tasks.

2009

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Natural Language Generation with Tree Conditional Random Fields
Wei Lu | Hwee Tou Ng | Wee Sun Lee
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Domain adaptive bootstrapping for named entity recognition
Dan Wu | Wee Sun Lee | Nan Ye | Hai Leong Chieu
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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A Generative Model for Parsing Natural Language to Meaning Representations
Wei Lu | Hwee Tou Ng | Wee Sun Lee | Luke S. Zettlemoyer
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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NUS-ML:Improving Word Sense Disambiguation Using Topic Features
Jun Fu Cai | Wee Sun Lee | Yee Whye Teh
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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Improving Word Sense Disambiguation Using Topic Features
Junfu Cai | Wee Sun Lee | Yee Whye Teh
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2005

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Learning Semantic Classes for Word Sense Disambiguation
Upali Sathyajith Kohomban | Wee Sun Lee
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)