Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587764
Jagadish S. Kallimani, K. Srinivasa, B. E. Reddy
The Information Extraction is a method for filtering information from large volumes of text. Information Extraction is a limited task than full text understanding. In full text understanding, we aspire to represent in an explicit fashion about all the information in a text. In contrast, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the output. In this paper, a model for summarization from large documents using a novel approach has been proposed. Extending the work for an Indian regional language (Kannada) and various analyses of results were discussed.
{"title":"Information retrieval by text summarization for an Indian regional language","authors":"Jagadish S. Kallimani, K. Srinivasa, B. E. Reddy","doi":"10.1109/NLPKE.2010.5587764","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587764","url":null,"abstract":"The Information Extraction is a method for filtering information from large volumes of text. Information Extraction is a limited task than full text understanding. In full text understanding, we aspire to represent in an explicit fashion about all the information in a text. In contrast, in Information Extraction, we delimit in advance, as part of the specification of the task and the semantic range of the output. In this paper, a model for summarization from large documents using a novel approach has been proposed. Extending the work for an Indian regional language (Kannada) and various analyses of results were discussed.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"20 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":"129165516","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587800
Ye Wu, F. Ren
Previous approaches of emotion recognition from text were mostly implemented under keyword-based or learning-based frameworks. However, keyword-based systems are unable to recognize emotion from text with no emotional keywords, and constructing an emotion lexicon is a tough work because of ambiguity in defining all emotional keywords. Completely prior-knowledge-free supervised machine learning methods for emotion recognition also do not perform as well as on some traditional tasks. In this paper, a fractionation training approach is proposed, utilizing the emotion lexicon extracted from an annotated blog emotion corpus to train SVM classifiers. Experimental results show the effectiveness of the proposed approach, and the use of some other experimental design also improves the classification accuracy.
{"title":"Improving emotion recognition from text with fractionation training","authors":"Ye Wu, F. Ren","doi":"10.1109/NLPKE.2010.5587800","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587800","url":null,"abstract":"Previous approaches of emotion recognition from text were mostly implemented under keyword-based or learning-based frameworks. However, keyword-based systems are unable to recognize emotion from text with no emotional keywords, and constructing an emotion lexicon is a tough work because of ambiguity in defining all emotional keywords. Completely prior-knowledge-free supervised machine learning methods for emotion recognition also do not perform as well as on some traditional tasks. In this paper, a fractionation training approach is proposed, utilizing the emotion lexicon extracted from an annotated blog emotion corpus to train SVM classifiers. Experimental results show the effectiveness of the proposed approach, and the use of some other experimental design also improves the classification accuracy.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"75 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":"128574637","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587865
C. Boitet, H. Blanchon, Mark Seligman, Valérie Bellynck
A Systran MT server became available on the minitel network in 1984, and on Internet in 1994. Since then we have come to a better understanding of the nature of MT systems by separately analyzing their linguistic, computational, and operational architectures. Also, thanks to the CxAxQ metatheorem, the systems' inherent limits have been clarified, and design choices can now be made in an informed manner according to the translation situations. MT evaluation has also matured: tools based on reference translations are useful for measuring progress; those based on subjective judgments for estimating future usage quality; and task-related objective measures (such as post-editing distances) for measuring operational quality. Moreover, the same technological advances that have led to “Web 2.0” have brought several futuristic predictions to fruition. Free Web MT services have democratized assimilation MT beyond belief. Speech translation research has given rise to usable systems for restricted tasks running on PDAs or on mobile phones connected to servers. New man-machine interface techniques have made interactive disambiguation usable in large-coverage multimodal MT. Increases in computing power have made statistical methods workable, and have led to the possibility of building low-linguistic-quality but still useful MT systems by machine learning from aligned bilingual corpora (SMT, EBMT). In parallel, progress has been made in developing interlingua-based MT systems, using hybrid methods. Unfortunately, many misconceptions about MT have spread among the public, and even among MT researchers, because of ignorance of the past and present of MT R&D. A compensating factor is the willingness of end users to freely contribute to building essential parts of the linguistic knowledge needed to construct MT systems, whether corpus-related or lexical. Finally, some developments we anticipated fifteen years ago have not yet materialized, such as online writing tools equipped with interactive disambiguation, and as a corollary the possibility of transforming source documents into self-explaining documents (SEDs) and of producing corresponding SEDs fully automatically in several target languages. These visions should now be realized, thanks to the evolution of Web programming and multilingual NLP techniques, leading towards a true Semantic Web, “Web 3.0”, which will support ubilingual (ubiquitous multilingual) computing.
{"title":"MT on and for the Web","authors":"C. Boitet, H. Blanchon, Mark Seligman, Valérie Bellynck","doi":"10.1109/NLPKE.2010.5587865","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587865","url":null,"abstract":"A Systran MT server became available on the minitel network in 1984, and on Internet in 1994. Since then we have come to a better understanding of the nature of MT systems by separately analyzing their linguistic, computational, and operational architectures. Also, thanks to the CxAxQ metatheorem, the systems' inherent limits have been clarified, and design choices can now be made in an informed manner according to the translation situations. MT evaluation has also matured: tools based on reference translations are useful for measuring progress; those based on subjective judgments for estimating future usage quality; and task-related objective measures (such as post-editing distances) for measuring operational quality. Moreover, the same technological advances that have led to “Web 2.0” have brought several futuristic predictions to fruition. Free Web MT services have democratized assimilation MT beyond belief. Speech translation research has given rise to usable systems for restricted tasks running on PDAs or on mobile phones connected to servers. New man-machine interface techniques have made interactive disambiguation usable in large-coverage multimodal MT. Increases in computing power have made statistical methods workable, and have led to the possibility of building low-linguistic-quality but still useful MT systems by machine learning from aligned bilingual corpora (SMT, EBMT). In parallel, progress has been made in developing interlingua-based MT systems, using hybrid methods. Unfortunately, many misconceptions about MT have spread among the public, and even among MT researchers, because of ignorance of the past and present of MT R&D. A compensating factor is the willingness of end users to freely contribute to building essential parts of the linguistic knowledge needed to construct MT systems, whether corpus-related or lexical. Finally, some developments we anticipated fifteen years ago have not yet materialized, such as online writing tools equipped with interactive disambiguation, and as a corollary the possibility of transforming source documents into self-explaining documents (SEDs) and of producing corresponding SEDs fully automatically in several target languages. These visions should now be realized, thanks to the evolution of Web programming and multilingual NLP techniques, leading towards a true Semantic Web, “Web 3.0”, which will support ubilingual (ubiquitous multilingual) computing.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"26 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":"120994756","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587847
Wei Zhang, Qiuhong Wang, Yeheng Deng, R. Du
In marine literature categorization, supervised machine learning method will take a lot of time for labelling the samples by hand. So we utilize Co-training method to decrease the quantities of labelled samples needed for training the classifier. In this paper, we only select features from the text details and add attribute labels to them. It can greatly boost the efficiency of text processing. For building up two views, we split features into two parts, each of which can form an independent view. One view is made up of the feature set of abstract, and the other is made up of the feature sets of title, keywords, creator and department. In experiments, the F1 value and error rate of the categorization system could reach about 0.863 and 14.26%.They are close to the performance of supervised classifier (0.902 and 9.13%), which is trained by more than 1500 labelled samples, however, the labelled samples used by Co-training categorization method to train the original classifier are only one positive sample and one negative sample. In addition we consider joining the idea of the active-learning in Co-training method.
{"title":"Marine literature categorization based on minimizing the labelled data","authors":"Wei Zhang, Qiuhong Wang, Yeheng Deng, R. Du","doi":"10.1109/NLPKE.2010.5587847","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587847","url":null,"abstract":"In marine literature categorization, supervised machine learning method will take a lot of time for labelling the samples by hand. So we utilize Co-training method to decrease the quantities of labelled samples needed for training the classifier. In this paper, we only select features from the text details and add attribute labels to them. It can greatly boost the efficiency of text processing. For building up two views, we split features into two parts, each of which can form an independent view. One view is made up of the feature set of abstract, and the other is made up of the feature sets of title, keywords, creator and department. In experiments, the F1 value and error rate of the categorization system could reach about 0.863 and 14.26%.They are close to the performance of supervised classifier (0.902 and 9.13%), which is trained by more than 1500 labelled samples, however, the labelled samples used by Co-training categorization method to train the original classifier are only one positive sample and one negative sample. In addition we consider joining the idea of the active-learning in Co-training method.","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":"115182144","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587775
Feiliang Ren, Jingbo Zhu, Huizhen Wang
This paper proposes a simple but powerful approach for obtaining technical term translation pairs in patent domain from Web automatically. First, several technical terms are used as seed queries and submitted to search engineering. Secondly, an extraction algorithm is proposed to extract some key word translation pairs from the returned web pages. Finally, a multi-feature based evaluation method is proposed to pick up those translation pairs that are true technical term translation pairs in patent domain. With this method, we obtain about 8,890,000 key word translation pairs which can be used to translate the technical terms in patent documents. And experimental results show that the precision of these translation pairs are more than 99%, and the coverage of these translation pairs for the technical terms in patent documents are more than 84%.
{"title":"Web-based technical term translation pairs mining for patent document translation","authors":"Feiliang Ren, Jingbo Zhu, Huizhen Wang","doi":"10.1109/NLPKE.2010.5587775","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587775","url":null,"abstract":"This paper proposes a simple but powerful approach for obtaining technical term translation pairs in patent domain from Web automatically. First, several technical terms are used as seed queries and submitted to search engineering. Secondly, an extraction algorithm is proposed to extract some key word translation pairs from the returned web pages. Finally, a multi-feature based evaluation method is proposed to pick up those translation pairs that are true technical term translation pairs in patent domain. With this method, we obtain about 8,890,000 key word translation pairs which can be used to translate the technical terms in patent documents. And experimental results show that the precision of these translation pairs are more than 99%, and the coverage of these translation pairs for the technical terms in patent documents are more than 84%.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"41 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":"121554019","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587837
M. Jinnai, Y. Akashi, S. Nakaya, F. Ren, M. Fukumi
In expressway companies, workers have been impacting signposts using wooden hammers and estimating the degree of the corrosion by listening to the sound. In order to automate this, we have been developing software that recognizes an abnormal impact vibrational response due to corrosion. This software extracts sonograms from impact vibrational waves using the LPC spectrum analysis, and matches images of the sonogram between a standard and an input impact vibrations using the Two-dimensional Geometric Distance. Then, the software distinguishes the abnormality of the input impact vibration using Wilcoxon rank-sum test. We have measured the impact vibrations of five normal signposts and five abnormal signposts, and carried out the automatic recognition experiments. As a result, the software has recognized correctly in all cases. We have verified the effectiveness of the proposed method.
{"title":"Recognition of abnormal vibrational responses of signposts using the Two-dimensional Geometric Distance and Wilcoxon test","authors":"M. Jinnai, Y. Akashi, S. Nakaya, F. Ren, M. Fukumi","doi":"10.1109/NLPKE.2010.5587837","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587837","url":null,"abstract":"In expressway companies, workers have been impacting signposts using wooden hammers and estimating the degree of the corrosion by listening to the sound. In order to automate this, we have been developing software that recognizes an abnormal impact vibrational response due to corrosion. This software extracts sonograms from impact vibrational waves using the LPC spectrum analysis, and matches images of the sonogram between a standard and an input impact vibrations using the Two-dimensional Geometric Distance. Then, the software distinguishes the abnormality of the input impact vibration using Wilcoxon rank-sum test. We have measured the impact vibrations of five normal signposts and five abnormal signposts, and carried out the automatic recognition experiments. As a result, the software has recognized correctly in all cases. We have verified the effectiveness of the proposed method.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"191 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":"116781424","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587843
Shiqi Li, T. Zhao, Hanjing Li, Shui Liu, Pengyuan Liu
This paper presents an cognitive approach to semantic role labeling in Chinese based on an extension of Construction-Integration (CI) model. The method can implicitly integrate more contextual and general knowledge into the calculating process in contrast with the machine learning methods. First, we define a proposition representation as the basic unit for semantic role labeling using CI model. Then the contextually appropriate propositions will be strengthened and inappropriate ones will be inhibited by simulating the spreading activation of human mind. Finally, experimental results show an encouraging performance on Chinese PropBank (CPB) and other two datasets.
{"title":"Using cognitive model to automatically analyze Chinese predicate","authors":"Shiqi Li, T. Zhao, Hanjing Li, Shui Liu, Pengyuan Liu","doi":"10.1109/NLPKE.2010.5587843","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587843","url":null,"abstract":"This paper presents an cognitive approach to semantic role labeling in Chinese based on an extension of Construction-Integration (CI) model. The method can implicitly integrate more contextual and general knowledge into the calculating process in contrast with the machine learning methods. First, we define a proposition representation as the basic unit for semantic role labeling using CI model. Then the contextually appropriate propositions will be strengthened and inappropriate ones will be inhibited by simulating the spreading activation of human mind. Finally, experimental results show an encouraging performance on Chinese PropBank (CPB) and other two datasets.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"20 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":"117129179","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587814
T. Tanioka, A. Kawamura, Mai Date, K. Osaka, Yuko Yasuhara, M. Kataoka, Yukie Iwasa, Toshihiro Sugiyama, Kazuyuki Matsumoto, Tomoko Kawata, Misako Satou, K. Mifune
At the “A” psychiatric hospital, previously nurses used paper-based nursing staffs' daily records. We aimed to manage the higher quality nursing and introduced “electronic management system for nursing staffs' daily records system (ENSDR)” interlocked with “Psychoms ®” into this hospital. Some good effects were achieved by introducing this system. However, some problems have been left in this system. The purpose of this study is to evaluate the current situation and challenges which brought out by using ENSDR, and to indicate the future direction of the development.
{"title":"Computerized electronic nursing staffs' daily records system in the “A” psychiatric hospital: Present situation and future prospects","authors":"T. Tanioka, A. Kawamura, Mai Date, K. Osaka, Yuko Yasuhara, M. Kataoka, Yukie Iwasa, Toshihiro Sugiyama, Kazuyuki Matsumoto, Tomoko Kawata, Misako Satou, K. Mifune","doi":"10.1109/NLPKE.2010.5587814","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587814","url":null,"abstract":"At the “A” psychiatric hospital, previously nurses used paper-based nursing staffs' daily records. We aimed to manage the higher quality nursing and introduced “electronic management system for nursing staffs' daily records system (ENSDR)” interlocked with “Psychoms ®” into this hospital. Some good effects were achieved by introducing this system. However, some problems have been left in this system. The purpose of this study is to evaluate the current situation and challenges which brought out by using ENSDR, and to indicate the future direction of the development.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"51 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":"127284984","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}
Pub Date : 2010-09-30DOI: 10.1109/NLPKE.2010.5587827
Peng Liu, Yu Zhou, Chengqing Zong
The bilingual language corpus has a great effect on the performance of a statistical machine translation system. More data will lead to better performance. However, more data also increase the computational load. In this paper, we propose methods to estimate the sentence weight and select more informative sentences from the training corpus and the development corpus based on the sentence weight. The translation system is built and tuned on the compact corpus. The experimental results show that we can obtain a competitive performance with much less data.
{"title":"Data selection for statistical machine translation","authors":"Peng Liu, Yu Zhou, Chengqing Zong","doi":"10.1109/NLPKE.2010.5587827","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587827","url":null,"abstract":"The bilingual language corpus has a great effect on the performance of a statistical machine translation system. More data will lead to better performance. However, more data also increase the computational load. In this paper, we propose methods to estimate the sentence weight and select more informative sentences from the training corpus and the development corpus based on the sentence weight. The translation system is built and tuned on the compact corpus. The experimental results show that we can obtain a competitive performance with much less data.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"392 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":"115992325","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}
Some users in a community site abuse the anonymity and attempt to manipulate communications in a community site. These users and their submissions discourage other users, keep them from retrieving good communication records, and decrease the credibility of the communication site. To solve this problem, we conducted an experimental study to detect users suspected of using multiple user accounts and manipulating evaluations in a community site. In this study, we used messages in the data of Yahoo! chiebukuro for data training and examination.
{"title":"Detection of users suspected of using multiple user accounts and manipulating evaluations in a community site","authors":"Naoki Ishikawa, Kenji Umemoto, Yasuhiko Watanabe, Yoshihiro Okada, Ryo Nishimura, M. Murata","doi":"10.1109/NLPKE.2010.5587765","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587765","url":null,"abstract":"Some users in a community site abuse the anonymity and attempt to manipulate communications in a community site. These users and their submissions discourage other users, keep them from retrieving good communication records, and decrease the credibility of the communication site. To solve this problem, we conducted an experimental study to detect users suspected of using multiple user accounts and manipulating evaluations in a community site. In this study, we used messages in the data of Yahoo! chiebukuro for data training and examination.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"243 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":"129773764","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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