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Zhong Su

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4 papers

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IJCAI Conference 2020 Conference Paper

Deep Semantic Compliance Advisor for Unstructured Document Compliance Checking

Honglei Guo
Bang An
Zhili Guo
Zhong Su

Unstructured document compliance checking is always a big challenge for banks since huge amounts of contracts and regulations written in natural language require professionals' interpretation and judgment. Traditional rule-based or keyword-based methods cannot precisely characterize the deep semantic distribution in the unstructured document semantic compliance checking due to the semantic complexity of contracts and regulations. Deep Semantic Compliance Advisor (DSCA) is an unstructured document compliance checking platform which provides multi-level semantic comparison by deep learning algorithms. In the statement-level semantic comparison, a Graph Neural Network (GNN) based syntactic sentence encoder is proposed to capture the complicate syntactic and semantic clues of the statement sentences. This GNN-based encoder outperforms existing syntactic sentence encoders in deep semantic comparison and is more beneficial for long sentences. In the clause-level semantic comparison, an attention-based semantic relatedness detection model is applied to find the most relevant legal clauses. DSCA significantly enhances the productivity of legal professionals in the unstructured document compliance checking for banks.

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IJCAI Conference 2019 Conference Paper

Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation

Yang Wei
Honglei Guo
Jinmao Wei
Zhong Su

A mind-map is a diagram used to represent ideas linked to and arranged around a central concept. It’s easier to visually access the knowledge and ideas by converting a text to a mind-map. However, highlighting the semantic skeleton of an article remains a challenge. The key issue is to detect the relations amongst concepts beyond intra-sentence. In this paper, we propose a multi-grained framework for automatic mind-map generation. That is, a novel neural network is taken to detect the relations at first, which employs multi-hop self-attention and gated recurrence network to reveal the directed semantic relations via sentences. A recursive algorithm is then designed to select the most salient sentences to constitute the hierarchy. The human-like mind-map is automatically constructed with the key phrases in the salient sentences. Promising results have been achieved on the comparison with manual mind-maps. The case studies demonstrate that the generated mind-maps reveal the underlying semantic structures of the articles.

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AAAI Conference 2015 Conference Paper

Sampling Representative Users from Large Social Networks

Jie Tang
Chenhui Zhang
Keke Cai
Li Zhang
Zhong Su

Finding a subset of users to statistically represent the original social network is a fundamental issue in Social Network Analysis (SNA). The problem has not been extensively studied in existing literature. In this paper, we present a formal definition of the problem of sampling representative users from social network. We propose two sampling models and theoretically prove their NP-hardness. To efficiently solve the two models, we present an efficient algorithm with provable approximation guarantees. Experimental results on two datasets show that the proposed models for sampling representative users significantly outperform (+6%-23% in terms of Precision@100) several alternative methods using authority or structure information only. The proposed algorithms are also effective in terms of time complexity. Only a few seconds are needed to sampling 300 representative users from a network of 100, 000 users. All data and codes are publicly available. 1

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AAAI Conference 2011 Conference Paper

Analyzing and Predicting Not-Answered Questions in Community-based Question Answering Services

Lichun Yang
Shenghua Bao
Qingliang Lin
Xian Wu
Dingyi Han
Zhong Su
Yong Yu

This paper focuses on analyzing and predicting not-answered questions in Community based Question Answering (CQA) services, such as Yahoo! Answers. In CQA, users express their information needs by submitting questions and await answers from other users. One of the key problems of this pattern is that sometimes no one helps to give answers. In this paper, we analyze the not-answered questions and give a ﬁrst try of predicting whether questions will receive answers. More speciﬁcally, we ﬁrst analyze the questions of Yahoo! Answers based on the features selected from different perspectives. Then, we formalize the prediction problem as supervised learning task and leverage the proposed features to make predictions. Extensive experiments are made on 76, 251 questions collected from Yahoo! Answers.

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