Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)最新文献_第2页

Are we waves or are we particles? A new insight into deep semantics in natural language processing 我们是波还是粒子?自然语言处理中深层语义的新见解

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587805

Svetlana Machova, J. Klecková

引用次数: 2

Shui nationality characters stroke shape input method 水族文字笔画形状输入法

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587840

Hanyue Yang, Xiaorong Chen

引用次数: 0

Chinese patent retrieval based on the pragmatic information 基于语用信息的中文专利检索

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587776

Liping Wu, Song Liu, F. Ren

引用次数: 1

Part-of-speech tagging for Chinese unknown words in a domain-specific small corpus using morphological and contextual rules 基于形态和语境规则的小语料库中文未知词词性标注

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587771

Tao-Hsing Chang, Fu-Yuan Hsu, Chia-Hoang Lee, Hahn-Ming Lee

引用次数: 1

Statistical parsing based on Maximal Noun Phrase pre-processing 基于最大名词短语预处理的统计分析

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587850

Qiaoli Zhou, Yue Gu, Xin Liu, Wenjing Lang, Dongfeng Cai

引用次数: 3

A reranking method for syntactic parsing with heterogeneous treebanks 异构树库句法分析的重排序方法

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587842

Haibo Ding, Muhua Zhu, Jingbo Zhu

引用次数: 0

Flexible English writing support based on negative-positive conversion method 基于正负转换法的灵活英语写作支持

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587778

Yasushi Katsura, Kazuyuki Matsumoto, F. Ren

引用次数: 1

Context-based term identification and extraction for ontology construction 面向本体构建的基于上下文的术语识别与提取

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587801

Hui-Ngo Goh, Ching Kiu

{"title":"Context-based term identification and extraction for ontology construction","authors":"Hui-Ngo Goh, Ching Kiu","doi":"10.1109/NLPKE.2010.5587801","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587801","url":null,"abstract":"Ontology construction often requires a domain specific corpus in conceptualizing the domain knowledge; specifically, it is an association of terms, relation between terms and related instances. It is a vital task to identify a list of significant term for constructing a practical ontology. In this paper, we present the use of a context-based term identification and extraction methodology for ontology construction from text document. The methodology is using a taxonomy and Wikipedia to support automatic term identification and extraction from structured documents with an assumption of candidate terms for a topic are often associated with its topic-specific keywords. A hierarchical relationship of super-topics and sub-topics is defined by a taxonomy, meanwhile, Wikipedia is used to provide context and background knowledge for topics that defined in the taxonomy to guide the term identification and extraction. The experimental results have shown the context-based term identification and extraction methodology is viable in defining topic concepts and its sub-concepts for constructing ontology. The experimental results have also proven its viability to be applied in a small corpus / text size environment in supporting ontology construction.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126280720","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}

引用次数: 2

A pragmatic model for new Chinese word extraction 汉语新词提取的语用模型

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587846

Haijun Zhang, Heyan Huang, Chao-Yong Zhu, Shumin Shi

引用次数: 7

Bagging to find better expansion words 寻找更好的扩展词

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587826

Bingqing Wang, Yaqian Zhou, Xipeng Qiu, Qi Zhang, Xuanjing Huang

{"title":"Bagging to find better expansion words","authors":"Bingqing Wang, Yaqian Zhou, Xipeng Qiu, Qi Zhang, Xuanjing Huang","doi":"10.1109/NLPKE.2010.5587826","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587826","url":null,"abstract":"The supervised learning has been applied into the query expansion techniques, which trains a model to predict the “goodness” or “utility” of the expanded term to the retrieval system. There are many features to measure the relatedness between the expanded word and the query, which can be incorporated in the supervised learning to select the expanded terms. The training data set is generated automatically by a tricky method. However, this method can be affected by many aspects. A severe problem is that the distribution of the features is query-dependent, which has not been discussed in previous work. With a different distribution on the features, it is questionable to merge these training instances together and use the whole data set to train one single model. In this paper, we first investigate the statistical distribution of the auto-generated training data and show the problems in the training data set. Based on our analysis, we proposed to use the bagging method to ensemble several regression models in order to get a better supervised model to make prediction on the expanded terms. We conducted the experiments on the TREC benchmark test collections. Our analysis on the training data reveals some interesting phenomena about the query expansion techniques. The experiment results also show that the bagging approach can achieve the state-of-art retrieval performance on the standard TREC data set.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125257444","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}

引用次数: 1