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A novel unsupervised method for new word extraction

Science China(information Sciences) 2016年第9期59卷 11-21页

作者： Lili MEI Heyan HUANG Xiaochi WEI Xianling MAO Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Department of Computer Science and TechnologyBeijing Institute of Technology

New words could benefit many NLP tasks such as sentence chunking and sentiment analysis. However, automatic new word extraction is a challenging task because new words usually have no fixed language pattern, and even appear with the new meanings of existing words. To tackle these problems, this paper proposes a novel method to extract new words. It not only considers domain specificity, but also combines with multiple statistical language knowledge. First, we perform a filtering algorithm to obtain a candidate list of new words. Then, we employ the statistical language knowledge to extract the top ranked new words. Experimental results show that our proposed method is able to extract a large number of new words both in Chinese and English corpus, and notably outperforms the state-of-the-art methods. Moreover, we also demonstrate our method increases the accuracy of Chinese word segmentation by 10% on corpus containing new words.

关键词： new word extraction word segmentation domain specificity statistical language knowledge domain word extraction

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A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation

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Tsinghua Science and Technology 2018年第6期23卷 660-670页

作者： Lerong Ma Lejian Liao DANDan Song Jingang Wang Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University

Knowledge Bases （KBs） are valuable resources of human knowledge which contribute to many applications. However, since they are manually maintained, there is a big lag between their contents and the upto-date information of entities. Considering a target entity in KBs, this paper investigates how Cumulative Citation Recommendation （CCR） can be used to effectively detect its worthy-citation documents in large volumes of stream data. Most global relevant models only consider semantic and temporat features of entity-document instances, which does not sufficiently exploit prior knowledge underlying entity-document instances. To tackle this problem, we present a Mixture of Experts （ME） model by introducing a latent layer to capture relationships between the entity-document instances and their latent class information. An extensive set of experiments was conducted on TREC-KBA-2013 dataset. The results show that the model can significantly achieve a better performance gain compared to state-of-the-art models in CCR.

关键词： knowledge base acceleration cumulative citation recommendation Mixture of Experts (ME) LatentEntity-Document Classes (LEDCs)

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Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning

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End States Guided Matching Network for Retrieval-based Multi-turn Conversation 3

End States Guided Matching Network for Retrieval-based Multi...

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2021年第三届先进计算机技术、信息科学与通信国际会议(CTISC2021)

作者： Weixin Tan Dandan Song Yujin Gao Lab of High Volume language Information Processing & Cloud Computing Beijing Lab of Intelligent Information TechnologyBeijing Institute of Technology

ISBN: (纸本)9781665418683

Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a proper ***, in some cases, a brief response like "ok" could be the more appropriate one. We find that it is a semantically ended conversation that a brief response usually comes after,so there is no need to provide any context-related information after that. Thus, in addition to match the response with context,it is also critical to recognize the state of whether a dialogue has ended, and learn how to get necessary information from context of different end states separately. To achieve this, we propose an end states guided matching network to determine and incorporate the end states by jointly consider the length of response and the local similarity between the response and last few utterances. In addition, we adopt multiple descriptive sequence representations for a more reliable matching *** results demonstrate that our model outperforms the state-of-the-art methods in multiple datasets.

关键词： information science Computational modeling Computer network reliability Reliability Data mining

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When Factorization Meets Heterogeneous Latent Topics： An Interpretable Cross-Site Recommendation Framework

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Journal of Computer Science & Technology 2015年第4期30卷 917-932页

作者：辛欣林钦佑魏骁驰黄河燕 Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing School of Computer Science Beijing Institute of Technology Beijing 100081 China Microsoft Research Asia Beijing 100080 China

Data sparsity is a well-known challenge in recommender systems. Previous studies alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we solve this challenge by exploring cross-site information. Specifically, we examine： 1） how to effectively and efficiently utilize cross-site ratings and content features to improve recommendation performance and 2） how to make the recommendation interpretable by utilizing content features. We propose a joint model of matrix factorization and latent topic analysis. Heterogeneous content features are modeled by multiple kinds of latent topics. In addition, the combination of matrix factorization and latent topics makes the recommendation result interpretable. Therefore, the above two issues are simultaneously solved. Through a real-world ***, where user behaviors in three social media sites are collected, we demonstrate that the proposed model is effective in improving recommendation performance and interpreting the rationale of ratings.

关键词： collaborative filtering recommender system topic analysis

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Improving Parallel Corpus Quality for Chinese-Vietnamese Statistical Machine Translation

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Journal of Beijing Institute of Technology 2018年第1期27卷 127-136页

作者： Huu-anh Tran Yuhang Guo Ping Jian Shumin Shi Heyan Huang Department of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Application Beijing Institute of Technology

The performance of a machine translation system heavily depends on the quantity and quality of the bilingual language resource. However,getting a parallel corpus,which has a large scale and is of high quality,is a very difficult task especially for low resource languages such as Chinese-Vietnamese. Fortunately,multilingual user generated contents（ UGC）,such as bilingual movie subtitles,provide us access to automatic construction of the parallel corpus. Although the amount of UGC parallel corpora can be considerable,the original corpus is not suitable for statistical machine translation（ SMT） systems. The corpus may contain translation errors,sentence mismatching,free translations,etc. To improve the quality of the bilingual corpus for SMT systems,three filtering methods are proposed： sentence length difference,the semantic of sentence pairs,and machine learning. Experiments are conducted on the Chinese to Vietnamese translation *** results demonstrate that all the three methods effectively improve the corpus quality,and the machine translation performance（ BLEU score） can be improved by 1. 32.

关键词： parallel corpus filtering low resource languages bilingual movie subtitles machine translation Chinese-Vietnamese translation

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A Hybrid Method of Domain Lexicon Construction for Opinion Targets Extraction Using Syntax and Semantics

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Journal of Computer Science & Technology 2016年第3期31卷 595-603页

作者： Chun Liao Chong Feng Sen Yang He-Yan Huang Department of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing 100081 China

Opinion targets extraction of Chinese microblogs plays an important role in opinion mining. There has been a significant progress in this area recently, especially the method based on conditional random field （CRF）. However, this method only takes lexicon-related features into consideration and does not excavate the implied syntactic and semantic knowledge. We propose a novel approach which incorporates domain lexicon with groups of syntactical and semantic features. The approach acquires domain lexicon through a novel way which explores syntactic and semantic information through Part- of-Speech, dependency structure, phrase structure, semantic role and semantic similarity based on word embedding. And then we combine the domain lexicon with opinion targets extracted from CRF with groups of features for opinion targets extraction. Experimental results on COAE2014 dataset show the outperformance of the approach compared with other well-known methods on the task of opinion targets extraction.

关键词： domain lexicon opinion targets extraction syntactic structure semantic role word embedding

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PSVM: a preference-enhanced SVM model using preference data for classification

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Science China(information Sciences) 2017年第12期60卷 165-178页

作者： Lerong MA Dandan SONG Lejian LIAO Jingang WANG Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science and Technology Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University Search Business Department Alibaba Group

Classification is an essential task in data mining, machine learning and pattern recognition *** classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine(PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization(SMO)algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation(KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI,Stat Lib and ***. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics.

关键词： preference SVM classification sampling sequential minimal optimization(SMO)

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Failure Handling of E-Business Services in an Extensible Constraint Service Model

Failure Handling of E-Business Services in an Extensible Con...

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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Ruoyu Chen Lejian Liao Zhi Fang Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications Beijing Key Laboratory of Intelligent Information TechnologySchool of Computer ScienceBeijing Institute of Technology

ISBN: (纸本)9781467349994

Failure handling of business transactions is essential in E-Business *** paper extends the service process specification proposed in a contract-centered constraint-based service modelling framework with failure handling *** failure in process model,generalized failure such as QoS violation can be modelled in *** semantics for failure handling in E-Business services as well as policies are discussed via a state transition system.A virtual machine is built for contracting and executing of services under this framework.

关键词： Services service Failure Handling of E-Business Services in an Extensible Constraint Service Model

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Incorporating target language semantic roles into a string-to-tree translation model

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Frontiers of information Technology & Electronic Engineering 2017年第10期18卷 1534-1542页

作者： Chao SU Yu-hang GUO He-yan HUANG Shu-min SHI Chong FENG School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing 100081China Beijing Advanced Innovation Center for Imaging Technology Capital Normal UniversityBeijing 100048China

The string-to-tree model is one of the most successful syntax-based statistical machine translation(SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model:(1) adding role labels in the syntax tree;(2) constructing a semantic role tree, and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora, and the two proposed methods surpass the phrase-based system on large-scale training data.

关键词： Machine translation Semantic role Syntax tree String-to-tree

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