Erscheinungsdatum: 08/2012, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Building Domain Ontologies and Automated Text Categorization, Titelzusatz: a contribution to NLP, Autor: Ray, Sukanya // Chandra, Nidhi, Verlag: LAP Lambert Academic Publishing, Sprache: Englisch, Rubrik: Informatik // EDV, Sonstiges, Seiten: 68, Informationen: Paperback, Gewicht: 118 gr, Verkäufer: averdo
NLP-Driven Document Representations for Text Categorization ab 48.99 € als Taschenbuch: Empirical Selection of NLP-Driven Document Representations for Text Categorization. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,
Building Domain Ontologies and Automated Text Categorization ab 48.99 € als Taschenbuch: a contribution to NLP. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,
NLP-Driven Document Representations for Text Categorization ab 48.99 EURO Empirical Selection of NLP-Driven Document Representations for Text Categorization
Text Categorization is the task of assigning predefined labels to textual documents. Current research has been focused on using word based representations called bag-of-words (BOW) with strong statistical learners. Few studies have explored the use of more complex Natural Language Processing (NLP) driven representations based on phrases, proper names and word senses. None of these had definitive results on these features? benefits for text categorization problems. This book studies the use of NLP-driven document representations captured at many different levels of language processing, and shows that NLP-driven document representations improve text categorization. A methodology, called ?Empirical Selection Methodology for NLP-driven document representations?, is presented. Methodology helps to select document representations for each category in the categorization problem. The methodology should help Text Categorization researchers as well as researchers working on other classification problems, because it is generalizable, and can produce better instance representations for different learning problems.
In recent years there has been a massive growth in textual information especially in the internet.While searching for some topic especially some new topic in the internet it will be easier if someone knows the pre-requisites and post-requisites of that topic. Often the topics are found without any proper title and it becomes difficult later on to find which document was for which topic.A text categorization method can provide solution to this problem. Text categorization means assigning an uncategorized document into one or more pre-defined categories.So far researches have focused on using word based representation called Bag-of-Words (BOW) with strong statistical users, some are based on complex NLP representation based on words, phrases, sentences,and word-sense.This book focuses on the construction of domain based ontology so that users can relate to different topics of a domain and an automated text categorization technique based on Term Frequency Inverse Document Frequency (tf -idf) method is proposed that will categorize the uncategorized documents.With this approach user can not only categorize documents but also visualize the relationship among the terms of the document
This book constitutes the refereed proceedings of the 33rd annual European Conference on Information Retrieval Research, ECIR 2011, held in Dublin, Ireland, in April 2010. The 45 revised full papers presented together with 24 poster papers, 17 short papers, and 6 tool demonstrations were carefully reviewed and selected from 223 full research paper submissions and 64 poster/demo submissions. The papers are organized in topical sections on text categorization, recommender systems, Web IR, IR evaluation, IR for Social Networks, cross-language IR, IR theory, multimedia IR, IR applications, interactive IR, and question answering /NLP.
EsTAL - Espana ~ for Natural Language Processing - continued on from the three previous conferences: FracTAL, held at the Universit¿ e de Franch-Comt¿ e, Besan¿ con (France) in December 1997, VexTAL, held at Venice International University, Ca ¿ Foscari (Italy), in November 1999, and PorTAL, held at the U- versidade do Algarve, Faro (Portugal), in June 2002. The main goals of these conferences have been: (i) to bring together the international NLP community; (ii) to strengthen the position of local NLP research in the international NLP community; and (iii) to provide a forum for discussion of new research and - plications. EsTAL contributed to achieving these goals and increasing the already high international standing of these conferences, largely due to its Program Comm- tee,composedofrenownedresearchersinthe?eldofnaturallanguageprocessing and its applications. This clearly contributed to the signi?cant number of papers submitted (72) by researchers from (18) di?erent countries. The scope of the conference was structured around the following main topics: (i)computational linguistics research (spoken and written language analysis and generation; pragmatics, discourse, semantics, syntax and morphology; lexical - sources; word sense disambiguation; linguistic, mathematical, and psychological models of language; knowledge acquisition and representation; corpus-based and statistical language modelling; machine translation and translation aids; com- tationallexicography),and(ii)monolingualandmultilingualintelligentlanguage processing and applications (information retrieval, extraction and question - swering; automatic summarization; document categorization; natural language interfaces; dialogue systems and evaluation of systems).
In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.