Hao Liu


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SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
Hao Tian | Can Gao | Xinyan Xiao | Hao Liu | Bolei He | Hua Wu | Haifeng Wang | Feng Wu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-training, despite the fact that they are widely used in traditional sentiment analysis approaches. In this paper, we introduce Sentiment Knowledge Enhanced Pre-training (SKEP) in order to learn a unified sentiment representation for multiple sentiment analysis tasks. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. Experiments on three kinds of sentiment tasks show that SKEP significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets. We release our code at https://github.com/baidu/Senta.

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Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification
Yunjie Ji | Hao Liu | Bolei He | Xinyan Xiao | Hua Wu | Yanhua Yu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural Document-level Multi-aspect Sentiment Classification (DMSC) usually requires a lot of manual aspect-level sentiment annotations, which is time-consuming and laborious. As document-level sentiment labeled data are widely available from online service, it is valuable to perform DMSC with such free document-level annotations. To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision. Specifically, we connect aspect-level and document-level sentiment by formulating this problem as multiple instance learning, providing a way to learn aspect-level classifier from the back propagation of document-level supervision. Two diversified regularizations are further introduced in order to avoid the overfitting on document-level signals during training. Diversified textual regularization encourages the classifier to select aspect-relevant snippets, and diversified sentimental regularization prevents the aspect-level sentiments from being overly consistent with document-level sentiment. Experimental results on TripAdvisor and BeerAdvocate datasets show that D-MILN remarkably outperforms recent weakly-supervised baselines, and is also comparable to the supervised method.


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Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
Yanchao Hao | Hao Liu | Shizhu He | Kang Liu | Jun Zhao
Proceedings of the 27th International Conference on Computational Linguistics

Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks. Simple questions constitute a large part of questions queried on the web, still being a challenge to QA systems. In this work, we propose to conduct pattern extraction and entity linking first, and put forward pattern revising procedure to mitigate the error propagation problem. In order to learn to rank candidate subject-predicate pairs to enable the relevant facts retrieval given a question, we propose to do joint fact selection enhanced by relation detection. Multi-level encodings and multi-dimension information are leveraged to strengthen the whole procedure. The experimental results demonstrate that our approach sets a new record in this task, outperforming the current state-of-the-art by an absolute large margin.


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N-gram Model for Chinese Grammatical Error Diagnosis
Jianbo Zhao | Hao Liu | Zuyi Bao | Xiaopeng Bai | Si Li | Zhiqing Lin
Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)

Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis.This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task. The experiment results show that the proposed method is good at correction of Chinese grammatical errors.


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Effective Selection of Translation Model Training Data
Le Liu | Yu Hong | Hao Liu | Xing Wang | Jianmin Yao
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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A Three-Step Deterministic Parser for Chinese Dependency Parsing
Kun Yu | Sadao Kurohashi | Hao Liu
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers


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Chinese Word Segmentation and Named Entity Recognition by Character Tagging
Kun Yu | Sadao Kurohashi | Hao Liu | Toshiaki Nakazawa
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing