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A Framework for Summarization of Multi-topic Web Sites

Yongzheng Zhang
2009 Knowledge engineering review (Print)  
This data-driven process uses text, electronic dictionaries, linguistic ontologies and structured and semi-structured information to acquire knowledge.  ...  Recently, thanks to the enormous growth of the Information Society, the Web has caught the interest of researchers who have started to use it as the corpus to develop Information Extraction and Knowledge  ...  It relies on linguistic regularities (linguistic patterns) of the English language and the information distribution computed from the Web to extract and select ontological entities.  ... 
doi:10.1017/s0269888909990312 fatcat:l5z2i3rcvrcjtgxbo7vmqcf3nm

Domain Ontology Learning from the Web

David Sánchez
2009 Knowledge engineering review (Print)  
This data-driven process uses text, electronic dictionaries, linguistic ontologies and structured and semi-structured information to acquire knowledge.  ...  Recently, thanks to the enormous growth of the Information Society, the Web has caught the interest of researchers who have started to use it as the corpus to develop Information Extraction and Knowledge  ...  It relies on linguistic regularities (linguistic patterns) of the English language and the information distribution computed from the Web to extract and select ontological entities.  ... 
doi:10.1017/s0269888909990300 fatcat:s72meo3nh5dujfz6et4pczq3ta

A Study on Ontology Based Abstractive Summarization

M. Jishma Mohan, C. Sunitha, Amal Ganesh, A. Jaya
2016 Procedia Computer Science  
With widespread use of Internet and the emergence of information aggregation on a large scale, a quality text summarization is essential to effectively condense the information.  ...  Automatic summarization systems condense the documents by extracting the most relevant facts. Summarization is commonly classified into two types, extractive and abstractive.  ...  Introduction Summarization is the process of extracting important information from the source text and to present that information to the user in the form of summary.  ... 
doi:10.1016/j.procs.2016.05.122 fatcat:e4d6t3x5bbalbjyhv77vbojtoq

Text-Mining: Application Development Challenges [chapter]

Sundar Varadarajan, Kas Kasravi, Ronen Feldman
2003 Applications and Innovations in Intelligent Systems X  
Special emphasis is placed on post-information extraction processing, such as improving the relevance of the extracted information, summarization models, techniques for handling typographical errors, resolution  ...  With focus on rule-based information extraction, and references to actual cases, the authors share their experiences from developing several text-mining applications in diverse industries.  ...  KNOWLEDGE-BASE CHALLENGES The cornerstone of a text-mining application is its linguistic knowledge-base, and how this knowledge is represented.  ... 
doi:10.1007/978-1-4471-0649-4_17 fatcat:ruq2vvv5xzbxtp5xl3bpuncjr4

Guest editors' introduction: Recent advances in natural language processing

F. Ciravegna, S. Harabagiu
2003 IEEE Intelligent Systems  
Acknowledgments Organizing a special issue is both difficult and tiring. We thank all those who submitted papers for this special issue.  ...  Space limitations and the necessity of representing a wide range of technologies caused the exclusion of some other excellent papers. They will be published in future issues of this magazine.  ...  "Personalizing Web Publishing via Information Extraction," by Roberto Basili, Alessandro Moschitti, Maria Teresa Pazienza, and Fabio Massimo Zanzotto, portrays a knowledge-based information extraction  ... 
doi:10.1109/mis.2003.1179188 fatcat:swj2tzd4bfbgtmlnkfcwmhf5ga

Page 470 of Computational Linguistics Vol. 24, Issue 3 [page]

1998 Computational Linguistics  
of interest using tools related to information extraction, conceptual combination, and text generation.  ...  First, there is already a large body of related research projects in in- formation extraction, knowledge representation, and text planning in the domain of terrorism.  ... 

An Automated Learner for Extracting New Ontology Relations

Amaal Saleh Hassan Al Hashimy, Narayanan Kulathuramaiyer
2012 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT)  
Lexical semantics is an important task with many potential applications including but not limited to, Information Retrieval, Information Extraction, Text Summarization, and Language Modeling.  ...  As this task "automatic recognition of semantic relations between pairs of words in text" can be used in many NLP applications, its implementation are demanding and may include many potential methodologies  ...  In recent years, the acquisition of ontologies from domain texts using machine learning and text mining methods has been proposed as a means of facilitating the ontology engineering process.  ... 
doi:10.1109/acsat.2012.95 fatcat:n35gxierijf3lh7cjsbhukzmyi

How to Deal with Heterogeneous Data?

Mathieu Roche
2015 Symposium on Information Management and Big Data  
These are extracted in textual documents using natural language processing (NLP) techniques based on linguistic and statistic information (Manning and Schütze, 1999): • The extraction of thematic information  ...  The proposed method consists of four stages: data acquisition, information retrieval (i.e. identification of relevant documents), information extraction (i.e. extraction of symptoms, locations, dates,  ... 
dblp:conf/simbig/Roche15 fatcat:yej5he2cy5fuhi2zu4ivrwhvya

Natural Language Processing

Matthew N. O. Sadiku, Yu Zhou, Sarhan M. Musa
2018 International Journal of Advances in Scientific Research and Engineering  
Natural language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It is a computational approach to text analysis.  ...  It is the application of a wide range of computational techniques for the understanding, automatic analysis, and representation of human language. This paper provides a brief introduction to NLP.  ...  These include classifying text into categories, indexing, automatic translation, speech understanding, information extraction, automatic summarization, knowledge acquisition, games, opinion mining, spell-checking  ... 
doi:10.31695/ijasre.2018.32708 fatcat:zngsgdxybrbmtdelpjiy6ixve4

Page 30 of Computational Linguistics Vol. 18, Issue 1 [page]

1992 Computational Linguistics  
Information extraction and text summarization using linguistic knowledge acquisition.” Information Processing and Management 25(4): 419-428. Ravin, Yael (1990).  ...  “Extracting company names from text.” In IEEE AI Applications Conference (CAIA). Rau, Lisa FE; Jacobs, Paul S.; and Zernik, Uri (1989).  ... 

On the Need to Bootstrap Ontology Learning with Extraction Grammar Learning [chapter]

Georgios Paliouras
2005 Lecture Notes in Computer Science  
, information extraction, grammar induction and ontology enrichment is presented.  ...  The main claim of this paper is that machine learning can help integrate the construction of ontologies and extraction grammars and lead us closer to the Semantic Web vision.  ...  Acknowledgments This paper includes ideas and work that are not solely of the author. A number of current and past SKEL members have been involved in the presented work.  ... 
doi:10.1007/11524564_8 fatcat:444chquckvck7n6mluzi4gaje4

First experiences of using semantic knowledge learned by ASIUM for information extraction task using INTEX

David Faure, Thierry Poibeau
2000 European Conference on Artificial Intelligence  
We describe a first experiment of coupling an information extraction system based and the machine learning system ASIUM.  ...  Our aim in this article is to show how semantic knowledge learned for a specific domain can help the creating of a powerful information extraction system.  ...  Rodde (Cristal-Gresec) and A. Balvet (Université Paris X) for their contribution during analysis of the results.  ... 
dblp:conf/ecai/FaureP00 fatcat:g7gbi6obnfb2xmuhxa7w3ertrm

Text mining tools [chapter]

A. Zanasi
2005 Text Mining and its Applications to Intelligence, CRM and Knowledge Management  
PolyAnalyst for Text™ performs semantic text analysis, record coding, identification and visualization of patterns and clusters of information, automated or manual taxonomy creation and editing, taxonomy-based  ...  forced or self-learning www.witpress.com, ISSN 1755-8336 (on-line) WIT Transactions on Text Mining and its Applications to Intelligence, CRM and Knowledge Management 317 IT requirements PolyAnalyst for  ...  Insight discoverer extractor (IDE) and skills cartridges (SC) IDE extracts knowledge from unstructured texts.  ... 
doi:10.2495/978-1-85312-995-7/21 fatcat:2w5273jjobghfmdgrxnjal22xy

Page 525 of Computational Linguistics Vol. 28, Issue 4 [page]

2002 Computational Linguistics  
Computational Linguistics, 24(3):469-500. Rau, Lisa F., Paul S. Jacobs, and Uri Zernik. 1989. Information extraction and text summarization using linguistic knowledge acquisition.  ...  Salton, Gerald, Amit Singhal, Mandar Mitra, and Chris Buckley. 1997. Automatic text structuring and summarization. Information Processing & Management, 33(2):193-207. Sharp, Bernadette. 1998.  ... 

Language Technologies Meet Ontology Acquisition [chapter]

Galia Angelova
2005 Lecture Notes in Computer Science  
There are principle obstacles to extract automatically coherent conceptualisations from raw texts: it is impossible to identify exactly the types and their instances as well as the word meanings which  ...  This paper overviews and analyses the on-going research attempts to apply language technologies to automatic ontology acquisition.  ...  We can certainly remind that the idea of automatic knowledge acquisition from text is an older dream.  ... 
doi:10.1007/11524564_25 fatcat:kupbv6skv5cb7mdja5jzyr667m
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