... preprocessing is a set of context vectors that are represented as content words of each context. Learning with Unlabeled Data for TextCategorizationUsingBootstrapping and Feature Projection ... our method is used in a text categorization task, building text categorization systems will become significantly faster and less expensive. 1 Introduction Text categorization is the task ... words tend to appear in similar contexts, we can compute the similarity by using contextual information. Words and contexts play complementary roles. Contexts are similar to the extent that...
... video database. However, text extraction presents a number of problems because the properties of text may vary, as well as the text sizes and the text fonts. Furthermore, texts may appear in a ... horizontal edges to obtain candidate text regions. Real Text regions are then identified using the support vector machine. Text regions usually have special texture features because they consist ... or compressed images. Text extraction from uncompressed image can be classified as either component-based or texture-based. For component-based text extraction methods, text regions are detected...
... make a preliminary textcategorization experiment to examine further our approach. We only use MI3 formula in word segmentation step for the next experiment. B. TextCategorization Experiment ... approaches performing text categorization task. Nevertheless, the best performance approach for English may not be the best one for Vietnamese. To find the most appropriate textcategorization approach ... Approaches to Text Categorization. Journal of Information Retrieval, Vol 1, No. 1/2, pp 67—88. [17] Yiming Yang, C.G. Chute. 1994. An example-based mapping method for text categorization...
... (2005; 2006) use a textual representation ofwords by collating all the glosses of the word asfound in some dictionary. Then, a binary text clas-sifier is trained using the textual representation ... Subjectivity analysis is the taskof identifying text that present opinions as op-posed to objective text that present factual in-formation (Wiebe, 2000). Text could be eitherwords, phrases, sentences, ... identified without consider-ing their context (Wiebe, 2000; Hatzivassiloglouand Wiebe, 2000; Banea et al., 2008). In the sec-ond category, the context of subjective text is used(Riloff and Wiebe, 2003;...
... interfaces (BCI). Thisparadigm is widely used to build letter-by-letter text input systems using BCI. Neverthe-less using a BCI-typewriter depending only onEEG responses will not be sufficiently ... 2011.c2011 Association for Computational LinguisticsAn ERP-based Brain-Computer Interface for text entry using Rapid Serial Visual Presentation and Language ModelingK.E. Hild◦,U. Orhan†,D. ... next letters to be typed be-come highly predictable in certain contexts, partic-ularly word-internally. In applications where text generation/typing speed is very slow, the impactof language...
... In this section, we define comparative keywords and extract comparative-sentence candidates by using those keywords. 3.1 Comparative keyword First of all, we classify comparative sentences ... 153–156,Suntec, Singapore, 4 August 2009.c2009 ACL and AFNLPExtracting Comparative Sentences from Korean Text Documents Us-ing Comparative Lexical Patterns and Machine Learning Techniques Seon Yang ... Abstract This paper proposes how to automatically identify Korean comparative sentences from text documents. This paper first investigates many comparative sentences referring to pre-vious...
... of text categoriza-tion. For the Na¨ıve Bayes classifier this increase issignificant.1 MotivationIn the process of automatic classifying documentsinto several predefined classes – text categorization (Sebastiani, ... which can cause con-fusion. However, these statements are yet to be ver-ified.Fragments and Text Categorization Jan Blaˇt´ak and Eva Mr´akov´a and Luboˇs Popel´ınsk´yKnowledge ... – text documents are usually seenas sets or bags of all the words that have appearedin a document, maybe after removing words in astop-list. In this paper we describe a novel approachto text...
... bestate-of-the-art.3 TextCategorization ExperimentsThis section describes in detail the four experi-mental settings for the textcategorization exper-iments.3.1 CorpusFor the textcategorization ... improve automatic text categorization. We investigate what impact keywords have on thetask by predicting text categories on the basis ofkeywords only, and by combining full -text repre-sentations ... improve text categorization. Insummary we show that a higher perfor-mance — as measured by micro-averagedF-measure on a standard text categoriza-tion collection — is achieved when thefull-text...
... 2000). Few similar comparative studies have been re-ported for TextCategorization (Li et al., 2003) so far in literature. Text categorization and Information Retrieval are tasks that sometimes ... Features to Improve TextCategorization Effectiveness, Journal of Intelligent Systems, Spe-cial Issue. Dejun Xue, Maosong Sun. 2003b. A Study on Feature Weighting in Chinese Text Categorization, ... acts as a pre-requisite step in most text information proc-essing tasks such as Information Retrieval (Baeza-Yates and Ribeiro-Neto, 1999) and Text Categorization (Sebastiani, 2002). It is...
... Strapparava. 2005. Cross language text categorization by acquiring multilingual domainmodels from comparable corpora. In Proc. of theACL Workshop on Building and Using Parallel Texts(in conjunction of ... solu-tion for the Cross-Language Text Categorization task. In particular, when bilingual dictionar-ies/repositories are available, the performance ofthe categorization gets close to that of ... for the other terms inthe lexicons. We evaluate the performance of thecross-lingual text categorization, using both theBoW Kernel and the Multilingual Domain Kernel,observing that also in...