Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)最新文献_第8页

Web-based technical term translation pairs mining for patent document translation 基于web的技术术语翻译对挖掘，用于专利文献翻译

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587775

Feiliang Ren, Jingbo Zhu, Huizhen Wang

引用次数: 5

iTree - Automating the construction of the narration tree of Hadiths (Prophetic Traditions) iTree -自动构建圣训(先知传统)的叙述树

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587810

Aqil M. Azmi, Nawaf Bin Badia

{"title":"iTree - Automating the construction of the narration tree of Hadiths (Prophetic Traditions)","authors":"Aqil M. Azmi, Nawaf Bin Badia","doi":"10.1109/NLPKE.2010.5587810","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587810","url":null,"abstract":"The two fundamental sources of Islamic legislation are Qur'an and the Hadith. The Hadiths, or Prophetic Traditions, are narrations originating from the sayings and conducts of Prophet Muhammad. Each Hadith starts with a list of narrators involved in transmitting it followed by the transmitted text. The Hadith corpus is extremely huge and runs into hundreds of volumes. Due to its legislative importance, Hadiths have been carefully scrutinized by hadith scholars. One way a scholar may grade a Hadith is by its narration chain and the individual narrators in the chain. In this paper we report on a system that automatically generates the transmission chains of a Hadith and graphically display it. Computationally, this is a challenging problem. The text of Hadith is in Arabic, a morphologically rich language; and each Hadith has its own peculiar way of listing narrators. Our solution involves parsing and annotating the Hadith text and identifying the narrators' names. We use shallow parsing along with a domain specific grammar to parse the Hadith content. Experiments on sample Hadiths show our approach to have a very good success rate.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"120 3‐4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132908081","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: 30

Information retrieval by text summarization for an Indian regional language 一种印度地区语言的文本摘要信息检索

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587764

Jagadish S. Kallimani, K. Srinivasa, B. E. Reddy

引用次数: 23

Computerized electronic nursing staffs' daily records system in the “A” psychiatric hospital: Present situation and future prospects “A”精神病医院护理人员计算机电子日常记录系统的现状与展望

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587814

T. Tanioka, A. Kawamura, Mai Date, K. Osaka, Yuko Yasuhara, M. Kataoka, Yukie Iwasa, Toshihiro Sugiyama, Kazuyuki Matsumoto, Tomoko Kawata, Misako Satou, K. Mifune

引用次数: 4

Chinese base phrases chunking based on latent semi-CRF model 基于潜在半crf模型的汉语基本短语分块

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587802

Xiao Sun, Xiaoli Nan

{"title":"Chinese base phrases chunking based on latent semi-CRF model","authors":"Xiao Sun, Xiaoli Nan","doi":"10.1109/NLPKE.2010.5587802","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587802","url":null,"abstract":"In the fields of Chinese natural language processing, recognizing simple and non-recursive base phrases is an important task for natural language processing applications, such as information processing and machine translation. Instead of rule-based model, we adopt the statistical machine learning method, newly proposed Latent semi-CRF model to solve the Chinese base phrase chunking problem. The Chinese base phrases could be treated as the sequence labeling problem, which involve the prediction of a class label for each frame in an unsegmented sequence. The Chinese base phrases have sub-structures which could not be observed in training data. We propose a latent discriminative model called Latent semi-CRF(Latent Semi Conditional Random Fields), which incorporates the advantages of LDCRF(Latent Dynamic Conditional Random Fields) and semi-CRF that model the sub-structure of a class sequence and learn dynamics between class labels, in detecting the Chinese base phrases. Our results demonstrate that the latent dynamic discriminative model compares favorably to Support Vector Machines, Maximum Entropy Model, and Conditional Random Fields(including LDCRF and semi-CRF) on Chinese base phrases chunking.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"4 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":"133124987","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: 13

Chinese semantic role labeling based on semantic knowledge 基于语义知识的汉语语义角色标注

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587821

Yanqiu Shao, Zhifang Sui, Ning Mao

引用次数: 2

A method for generating document summary using field association knowledge and subjectively information 一种利用领域关联知识和主观信息生成文档摘要的方法

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587853

Abdunabi Ubul, E. Atlam, K. Morita, M. Fuketa, J. Aoe

引用次数: 1

A new method for solving context ambiguities using field association knowledge 一种利用领域关联知识解决上下文歧义的新方法

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587858

Li Wang, E. Atlam, M. Fuketa, K. Morita, J. Aoe

引用次数: 0

Realization of a high performance bilingual OCR system for Thai-English printed documents 高性能泰英双语OCR系统的实现

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587781

S. Tangwongsan, Buntida Suvacharakulton

{"title":"Realization of a high performance bilingual OCR system for Thai-English printed documents","authors":"S. Tangwongsan, Buntida Suvacharakulton","doi":"10.1109/NLPKE.2010.5587781","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587781","url":null,"abstract":"This paper presents a high performance bilingual OCR system for printed Thai and English text. With the complex nature of both Thai and English languages, the first stage is to identify languages within different zones by using geometric properties for differentiation. The second stage is the process of character recognition, in which the technique developed includes a feature extractor and a classifier. In the feature extraction, the thinned character image is analyzed and categorized into groups. Next, the classifier will take in two steps of recognition: the coarse level, followed by the fine level with a guide of decision trees. As to obtain an even better result, the final stage attempts to make use of dictionary look-up as to check for accuracy improvement in an overall performance. For verification, the system is tested by a series of experiments with printed documents in 141 pages and over 280,000 characters, the result shows that the system could obtain an accuracy of 100% in Thai monolingual, 98.18% in English monolingual, and 99.85% in bilingual documents on the average. In the final stage with a dictionary look-up, the system could yield a better accuracy of improvement up to 99.98% in bilingual documents as expected.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"56 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":"121132894","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

Document expansion using relevant web documents for spoken document retrieval 文档扩展使用相关的网络文档为口语文档检索

Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010) Pub Date : 2010-09-30 DOI: 10.1109/NLPKE.2010.5587854

Ryo Masumura, A. Ito, Yu Uno, Masashi Ito, S. Makino

{"title":"Document expansion using relevant web documents for spoken document retrieval","authors":"Ryo Masumura, A. Ito, Yu Uno, Masashi Ito, S. Makino","doi":"10.1109/NLPKE.2010.5587854","DOIUrl":"https://doi.org/10.1109/NLPKE.2010.5587854","url":null,"abstract":"Recently, automatic indexing of a spoken document using a speech recognizer attracts attention. However, index generation from an automatic transcription has many problems because the automatic transcription has many recognition errors and Out-Of-Vocabulary words. To solve this problem, we propose a document expansion method using Web documents. To obtain important keywords which included in the spoken document but lost by recognition errors, we acquire Web documents relevant to the spoken document. Then, an index of the spoken document is generated by combining an index that generated from the automatic transcription and the Web documents. We propose a method for retrieval of relevant documents, and the experimental result shows that the retrieved Web document contained many OOV words. Next, we propose a method for combining the recognized index and the Web index. The experimental result shows that the index of the spoken document generated by the document expansion was closer to an index from the manual transcription than the index generated by the conventional method. Finally, we conducted a spoken document retrieval experiment, and the document-expansion-based index gave better retrieval precision than the conventional indexing method.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"27 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121007971","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: 3