Author name cluster

Kenny Zhu

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5 papers

1 author row

AAAI Conference 2018 Conference Paper

Automatic Generation of Text Descriptive Comments for Code Blocks

Yuding Liang
Kenny Zhu

We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on ﬁxed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code- RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) signiﬁcantly higher than other learningbased approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required. 1

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AAAI Conference 2018 Conference Paper

Cross-Lingual Entity Linking for Web Tables

Xusheng Luo
Kangqi Luo
Xianyang Chen
Kenny Zhu

This paper studies the problem of linking string mentions from web tables in one language to the corresponding named entities in a knowledge base written in another language, which we call the cross-lingual table linking task. We present a joint statistical model to simultaneously link all mentions that appear in one table. The framework is based on neural networks, aiming to bridge the language gap by vector space transformation and a coherence feature that captures the correlations between entities in one table. Experimental results report that our approach improves the accuracy of cross-lingual table linking by a relative gain of 12. 1%. Detailed analysis of our approach also shows a positive and important gain brought by the joint framework and coherence feature. 1

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KR Conference 2016 Conference Paper

Commonsense Causal Reasoning between Short Texts

Zhiyi Luo
Yuchen Sha
Kenny Zhu
Seung-won Hwang
Zhongyuan Wang

Commonsense causal reasoning is the process of capturing and understanding the causal dependencies amongst events and actions. Such events and actions can be expressed in terms, phrases or sentences in natural language text. Therefore, one possible way of obtaining causal knowledge is by extracting causal relations between terms or phrases from a large text corpus. However, causal relations in text are sparse, ambiguous, and sometimes implicit, and thus difﬁcult to obtain. This paper attacks the problem of commonsense causality reasoning between short texts (phrases and sentences) using a data driven approach. We propose a framework that automatically harvests a network of causal-effect terms from a large web corpus. Backed by this network, we propose a novel and effective metric to properly model the causality strength between terms. We show these signals can be aggregated for causality reasonings between short texts, including sentences and phrases. In particular, our approach outperforms all previously reported results in the standard SEMEVAL COPA task by substantial margins.

AAAI Conference 2016 Conference Paper

Representing Verbs as Argument Concepts

Yu Gong
Kaiqi Zhao
Kenny Zhu

Verbs play an important role in the understanding of natural language text. This paper studies the problem of abstracting the subject and object arguments of a verb into a set of noun concepts, known as the “argument concepts”. This set of concepts, whose size is parameterized, represents the ﬁnegrained semantics of a verb. For example, the object of “enjoy” can be abstracted into time, hobby and event, etc. We present a novel framework to automatically infer human readable and machine computable action concepts with high accuracy.

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AAAI Conference 2015 Conference Paper

An Association Network for Computing Semantic Relatedness

Keyang Zhang
Kenny Zhu
Seung-won Hwang

To judge how much a pair of words (or texts) are semantically related is a cognitive process. However, previous algorithms for computing semantic relatedness are largely based on co-occurrences within textual windows, and do not actively leverage cognitive human perceptions of relatedness. To bridge this perceptional gap, we propose to utilize free association as signals to capture such human perceptions. However, free association, being manually evaluated, has limited lexical coverage and is inherently sparse. We propose to expand lexical coverage and overcome sparseness by constructing an association network of terms and concepts that combines signals from free association norms and five types of cooccurrences extracted from the rich structures of Wikipedia. Our evaluation results validate that simple algorithms on this network give competitive results in computing semantic relatedness between words and between short texts.

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