Minglei Li


2017

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Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis
Minglei Li | Qin Lu | Yunfei Long
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases. We examine how phrases can be represented through manually prepared lexicons, extended lexicons using computational methods, or word embedding. Comparative studies clearly show that word embedding using unsupervised distributional method outperforms manually prepared lexicons no matter what affective models are used in the lexicons. Our conclusion is that although different affective lexicons are cognitively backed by theories, they do not show any advantage over the automatically obtained word embedding.

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Fake News Detection Through Multi-Perspective Speaker Profiles
Yunfei Long | Qin Lu | Rong Xiang | Minglei Li | Chu-Ren Huang
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.

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A Cognition Based Attention Model for Sentiment Analysis
Yunfei Long | Qin Lu | Rong Xiang | Minglei Li | Chu-Ren Huang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.

2016

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Emotion Corpus Construction Based on Selection from Hashtags
Minglei Li | Yunfei Long | Lu Qin | Wenjie Li
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The availability of labelled corpus is of great importance for supervised learning in emotion classification tasks. Because it is time-consuming to manually label text, hashtags have been used as naturally annotated labels to obtain a large amount of labelled training data from microblog. However, natural hashtags contain too much noise for it to be used directly in learning algorithms. In this paper, we design a three-stage semi-automatic method to construct an emotion corpus from microblogs. Firstly, a lexicon based voting approach is used to verify the hashtag automatically. Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels. Finally, the remaining data will be manually examined to filter out the noisy data. Out of about 48K filtered Chinese microblogs, 39k microblogs are selected to form the final corpus with the Kappa value reaching over 0.92 for the automatic parts and over 0.81 for the manual part. The proportion of automatic selection reaches 54.1%. Thus, the method can reduce about 44.5% of manual workload for acquiring quality data. Experiment on a classifier trained on this corpus shows that it achieves comparable results compared to the manually annotated NLP&CC2013 corpus.

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Syllable based DNN-HMM Cantonese Speech to Text System
Timothy Wong | Claire Li | Sam Lam | Billy Chiu | Qin Lu | Minglei Li | Dan Xiong | Roy Shing Yu | Vincent T.Y. Ng
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model. This is a part of an effort in building a STT system to aid dyslexic students who have cognitive deficiency in writing skills but have no problem expressing their ideas through speech. For Cantonese speech recognition, the basic unit of acoustic models can either be the conventional Initial-Final (IF) syllables, or the Onset-Nucleus-Coda (ONC) syllables where finals are further split into nucleus and coda to reflect the intra-syllable variations in Cantonese. By using the Kaldi toolkit, our system is trained using the stochastic gradient descent optimization model with the aid of GPUs for the hybrid Deep Neural Network and Hidden Markov Model (DNN-HMM) with and without I-vector based speaker adaptive training technique. The input features of the same Gaussian Mixture Model with speaker adaptive training (GMM-SAT) to DNN are used in all cases. Experiments show that the ONC-based syllable acoustic modeling with I-vector based DNN-HMM achieves the best performance with the word error rate (WER) of 9.66% and the real time factor (RTF) of 1.38812.

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Event Based Emotion Classification for News Articles
Minglei Li | Da Wang | Qin Lu | Yunfei Long
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers