In this paper, we describe an approach to annotate the propositions in the Penn Chinese Treebank. We describe how diathesis alternation patterns can be used to make coarse sense distinctions for Chinese verbs as a necessary step in annotating the predicate-structure of Chinese verbs. We then discuss the representation scheme we use to label the semantic arguments and adjuncts of the predicates. We discuss several complications for this type of annotation and describe our solutions. We then discuss how a lexical database with predicate-argument structure information can be used to ensure consistent annotation. Finally, we discuss possible applications for this resource.
{"title":"Annotating the Propositions in the Penn Chinese Treebank","authors":"Nianwen Xue, Martha Palmer","doi":"10.3115/1119250.1119257","DOIUrl":"https://doi.org/10.3115/1119250.1119257","url":null,"abstract":"In this paper, we describe an approach to annotate the propositions in the Penn Chinese Treebank. We describe how diathesis alternation patterns can be used to make coarse sense distinctions for Chinese verbs as a necessary step in annotating the predicate-structure of Chinese verbs. We then discuss the representation scheme we use to label the semantic arguments and adjuncts of the predicates. We discuss several complications for this type of annotation and describe our solutions. We then discuss how a lexical database with predicate-argument structure information can be used to ensure consistent annotation. Finally, we discuss possible applications for this resource.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117232213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Semantic Knowledge-base of Contemporary Chinese (SKCC) is a large scale Chinese semantic resource developed by the Institute of Computational Linguistics of Peking University. It provides a large amount of semantic information such as semantic hierarchy and collocation features for 66,539 Chinese words and their English counterparts. Its POS and semantic classification represent the latest progress in Chinese linguistics and language engineering. The descriptions of semantic attributes are fairly thorough, comprehensive and authoritative. The paper introduces the outline of SKCC, and indicates that it is effective for word sense disambiguation in MT applications and is likely to be important for general Chinese language processing.
{"title":"The semantic Knowledge-base of Contemporary Chinese and Its Applications in WSD","authors":"Hui Wang, Shiwen Yu","doi":"10.3115/1119250.1119266","DOIUrl":"https://doi.org/10.3115/1119250.1119266","url":null,"abstract":"The Semantic Knowledge-base of Contemporary Chinese (SKCC) is a large scale Chinese semantic resource developed by the Institute of Computational Linguistics of Peking University. It provides a large amount of semantic information such as semantic hierarchy and collocation features for 66,539 Chinese words and their English counterparts. Its POS and semantic classification represent the latest progress in Chinese linguistics and language engineering. The descriptions of semantic attributes are fairly thorough, comprehensive and authoritative. The paper introduces the outline of SKCC, and indicates that it is effective for word sense disambiguation in MT applications and is likely to be important for general Chinese language processing.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128486806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a Chinese word tokenization system through HMM-based chunking. Experiments show that such a system can well deal with the unknown word problem in Chinese word tokenization.
{"title":"Chunking-based Chinese Word Tokenization","authors":"Guodong Zhou","doi":"10.3115/1119250.1119281","DOIUrl":"https://doi.org/10.3115/1119250.1119281","url":null,"abstract":"This paper introduces a Chinese word tokenization system through HMM-based chunking. Experiments show that such a system can well deal with the unknown word problem in Chinese word tokenization.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128649444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an unsupervised training approach to resolving overlapping ambiguities in Chinese word segmentation. We present an ensemble of adapted Naive Bayesian classifiers that can be trained using an unlabelled Chinese text corpus. These classifiers differ in that they use context words within windows of different sizes as features. The performance of our approach is evaluated on a manually annotated test set. Experimental results show that the proposed approach achieves an accuracy of 94.3%, rivaling the rule-based and supervised training methods.
{"title":"Unsupervised Training for Overlapping Ambiguity Resolution in Chinese Word Segmentation","authors":"Mu Li, Jianfeng Gao, C. Huang, Jianfeng Li","doi":"10.3115/1119250.1119251","DOIUrl":"https://doi.org/10.3115/1119250.1119251","url":null,"abstract":"This paper proposes an unsupervised training approach to resolving overlapping ambiguities in Chinese word segmentation. We present an ensemble of adapted Naive Bayesian classifiers that can be trained using an unlabelled Chinese text corpus. These classifiers differ in that they use context words within windows of different sizes as features. The performance of our approach is evaluated on a manually annotated test set. Experimental results show that the proposed approach achieves an accuracy of 94.3%, rivaling the rule-based and supervised training methods.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115667153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a new approach to segmentation of utterances into sentences using a new linguistic model based upon Maximum-entropy-weighted Bi-directional N-grams. The usual N-gram algorithm searches for sentence boundaries in a text from left to right only. Thus a candidate sentence boundary in the text is evaluated mainly with respect to its left context, without fully considering its right context. Using this approach, utterances are often divided into incomplete sentences or fragments. In order to make use of both the right and left contexts of candidate sentence boundaries, we propose a new linguistic modeling approach based on Maximum-entropy-weighted Bi-directional N-grams. Experimental results indicate that the new approach significantly outperforms the usual N-gram algorithm for segmenting both Chinese and English utterances.
{"title":"Utterance Segmentation Using Combined Approach Based on Bi-directional N-gram and Maximum Entropy","authors":"Ding Liu, Chengqing Zong","doi":"10.3115/1119250.1119253","DOIUrl":"https://doi.org/10.3115/1119250.1119253","url":null,"abstract":"This paper proposes a new approach to segmentation of utterances into sentences using a new linguistic model based upon Maximum-entropy-weighted Bi-directional N-grams. The usual N-gram algorithm searches for sentence boundaries in a text from left to right only. Thus a candidate sentence boundary in the text is evaluated mainly with respect to its left context, without fully considering its right context. Using this approach, utterances are often divided into incomplete sentences or fragments. In order to make use of both the right and left contexts of candidate sentence boundaries, we propose a new linguistic modeling approach based on Maximum-entropy-weighted Bi-directional N-grams. Experimental results indicate that the new approach significantly outperforms the usual N-gram algorithm for segmenting both Chinese and English utterances.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces an efficient analyser for the Chinese language, which efficiently and effectively integrates word segmentation, part-of-speech tagging, partial parsing and full parsing. The Chinese efficient analyser is based on a Hidden Markov Model (HMM) and an HMM-based tagger. That is, all the components are based on the same HMM-based tagging engine. One advantage of using the same single engine is that it largely decreases the code size and makes the maintenance easy. Another advantage is that it is easy to optimise the code and thus improve the speed while speed plays a critical important role in many applications. Finally, the performances of all the components can benefit from the optimisation of existing algorithms and/or adoption of better algorithms to a single engine. Experiments show that all the components can achieve state-of-art performances with high efficiency for the Chinese language.
{"title":"A Chinese Efficient Analyser Integrating Word Segmentation, Part-Of-Speech Tagging, Partial Parsing and Full Parsing","authors":"Guodong Zhou, Jian Su","doi":"10.3115/1119250.1119261","DOIUrl":"https://doi.org/10.3115/1119250.1119261","url":null,"abstract":"This paper introduces an efficient analyser for the Chinese language, which efficiently and effectively integrates word segmentation, part-of-speech tagging, partial parsing and full parsing. The Chinese efficient analyser is based on a Hidden Markov Model (HMM) and an HMM-based tagger. That is, all the components are based on the same HMM-based tagging engine. One advantage of using the same single engine is that it largely decreases the code size and makes the maintenance easy. Another advantage is that it is easy to optimise the code and thus improve the speed while speed plays a critical important role in many applications. Finally, the performances of all the components can benefit from the optimisation of existing algorithms and/or adoption of better algorithms to a single engine. Experiments show that all the components can achieve state-of-art performances with high efficiency for the Chinese language.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129215450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective self-organizing techniques for constructing monolingual semantic maps of Japanese and Chinese have already been developed. By extending the monolingual map to a bilingual semantic map, we have proposed a semantics-based approach for word alignment in a Japanese/Chinese bilingual corpus.
{"title":"Semantic Maps for Word Alignment in Bilingual Parallel Corpora","authors":"Q. Ma, Yujie Zhang, M. Murata, H. Isahara","doi":"10.3115/1119250.1119264","DOIUrl":"https://doi.org/10.3115/1119250.1119264","url":null,"abstract":"Effective self-organizing techniques for constructing monolingual semantic maps of Japanese and Chinese have already been developed. By extending the monolingual map to a bilingual semantic map, we have proposed a semantics-based approach for word alignment in a Japanese/Chinese bilingual corpus.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131201339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents our recent work for participation in the First International Chinese Word Segmentation Bake-off (ICWSB-1). It is based on a general-purpose ngram model for word segmentation and a case-based learning approach to disambiguation. This system excels in identifying in-vocabulary (IV) words, achieving a recall of around 96-98%. Here we present our strategies for language model training and disambiguation rule learning, analyze the system's performance, and discuss areas for further improvement, e.g., out-of-vocabulary (OOV) word discovery.
{"title":"Integrating Ngram Model and Case-based Learning for Chinese Word Segmentation","authors":"C. Kit, Zhiming Xu, J. Webster","doi":"10.3115/1119250.1119274","DOIUrl":"https://doi.org/10.3115/1119250.1119274","url":null,"abstract":"This paper presents our recent work for participation in the First International Chinese Word Segmentation Bake-off (ICWSB-1). It is based on a general-purpose ngram model for word segmentation and a case-based learning approach to disambiguation. This system excels in identifying in-vocabulary (IV) words, achieving a recall of around 96-98%. Here we present our strategies for language model training and disambiguation rule learning, analyze the system's performance, and discuss areas for further improvement, e.g., out-of-vocabulary (OOV) word discovery.","PeriodicalId":403123,"journal":{"name":"Workshop on Chinese Language Processing","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122770018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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