01966nas a2200217   4500008004100000245005200041210005200093260000900145300001400154490000700168520130000175653001901475653002101494653002701515653002401542653002801566100001401594700001101608700001201619856011701631       2012                        eng d00aWord combination kernel for text categorization0 aWord combination kernel for text categorization  c2012  a202 - 2110 v103 aWe proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.10aKernel methods10aMachine learning10aSupport vector machine10aText classification10aWord-combination kernel1 aZhang, L.1 aHu, X.1 aQin, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932&partnerID=40&md5=8649dfc8c1ee23ca70f2b137a74a19e401872nas a2200205   4500008004100000245007300041210006900114260000900183300001400192490000600206520120000212653002101412653001801433653002301451653002301474653001601497100001801513700001801531856011701549       2011                        eng d00aDetecting human sentiment from text using a proximity-based approach0 aDetecting human sentiment from text using a proximitybased appro  c2011  a206 - 2120 v93 aSentiment analysis seeks to characterize opinionated or evaluative aspects of natural language text thus helping people to discover valuable information from large amounts of unstructured data. Sentiment analysis can be used for grouping search engine results, analyzing news content, reviews for books, movie, sports, blogs, web forums, etc. Several methods have been proposed for sentiment analysis at word, sentence, and document levels mostly based on common machine learning techniques. In this paper we explore a new methodology for sentiment analysis by considering a new set of features based on word proximities in a written text. We propose three proximity-based features, namely, proximity distribution, mutual information between proximity types, and proximity patterns. We applied this approach to the analysis of movie reviews, drug reviews, and music reviews. Our experimental results show that proximity-based sentiment analysis is able to extract sentiments from a specific domain, with performance comparable to the state-of-the-art. To the best of our knowledge, this is the first attempt at focusing on only proximity-based features as the primary cues in sentiment analysis.10aMachine learning10aMovie reviews10aProximity analysis10aSentiment analysis10aText mining1 aHasan, S.M.S.1 aAdjeroh, D.A.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84855397249&partnerID=40&md5=a009c46b7b54e127e88ca882940b976702545nas a2200241   4500008004100000245011500041210006900156260000900225300001400234490000600248520174100254653002501995653002702020653001402047653002402061653002102085100001702106700001902123700001402142700001402156700001602170856011702186       2010                        eng d00aEnabling automatic just-in-time evaluation of in-class discussions in on-line collaborative learning practices0 aEnabling automatic justintime evaluation of inclass discussions   c2010  a322 - 3290 v83 aLearning by discussion when applied to on-line collaborative learning settings can provide significant benefits for students in education in general. Indeed, the discussion process plays an important social task in collaborative learning practices. Participants can discuss about the activity being performed, collaborate with each other through the exchange of ideas that may arise, propose new resolution mechanisms, justify and refine their own contributions, and as a result, acquire new knowledge. Considering these benefits, current educational organizations incorporate on-line discussions into web-based courses as part of the very rationale of their pedagogical models. However, in-class collaborative assignments are usually greatly participated and contributed, which makes the monitoring and assessment tasks by tutors and moderators time-consuming, tedious and error-prone. Specially hard if not impossible by human tutors is to manually deal with the sequences of hundreds of contributions making up the discussion threads and the relations between these contributions. Consequently, tutoring tasks during on-line discussions usually restrict to offer evaluation results of the contributing effort and quality after the collaborative learning activity takes place and thus neglect the essential issue of constantly considering the process of knowledge building while it is still being performed. In this paper, we propose a multidimensional model based on data analysis from online collaborative discussion interactions that provides a first step towards an automatic evaluation in just-in-time fashion. The context of this study is a real on-line discussion experience that took place at the Open University of Catalonia.10aAutomatic evaluation10aCollaborative learning10aGroupware10aKnowledge discovery10aMachine learning1 aCaballé, S.1 aLapedriza, À.1 aMasip, D.1 aXhafa, F.1 aAbraham, A.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960216812&partnerID=40&md5=0f38528ca7e6fa26bdcaf9c0b81206c902545nas a2200241   4500008004100000245011500041210006900156260000900225300001400234490000600248520174100254653002501995653002702020653001402047653002402061653002102085100001702106700001902123700001402142700001402156700001602170856011702186       2009                        eng d00aEnabling automatic just-in-time evaluation of in-class discussions in on-line collaborative learning practices0 aEnabling automatic justintime evaluation of inclass discussions   c2009  a289 - 2960 v73 aLearning by discussion when applied to on-line collaborative learning settings can provide significant benefits for students in education in general. Indeed, the discussion process plays an important social task in collaborative learning practices. Participants can discuss about the activity being performed, collaborate with each other through the exchange of ideas that may arise, propose new resolution mechanisms, justify and refine their own contributions, and as a result, acquire new knowledge. Considering these benefits, current educational organizations incorporate on-line discussions into web-based courses as part of the very rationale of their pedagogical models. However, in-class collaborative assignments are usually greatly participated and contributed, which makes the monitoring and assessment tasks by tutors and moderators time-consuming, tedious and error-prone. Specially hard if not impossible by human tutors is to manually deal with the sequences of hundreds of contributions making up the discussion threads and the relations between these contributions. Consequently, tutoring tasks during on-line discussions usually restrict to offer evaluation results of the contributing effort and quality after the collaborative learning activity takes place and thus neglect the essential issue of constantly considering the process of knowledge building while it is still being performed. In this paper, we propose a multidimensional model based on data analysis from online collaborative discussion interactions that provides a first step towards an automatic evaluation in just-in-time fashion. The context of this study is a real on-line discussion experience that took place at the Open University of Catalonia.10aAutomatic evaluation10aCollaborative learning10aGroupware10aKnowledge discovery10aMachine learning1 aCaballé, S.1 aLapedriza, À.1 aMasip, D.1 aXhafa, F.1 aAbraham, A.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-77953238102&partnerID=40&md5=6dce6350dd861170e2133b9b2f59762701542nas a2200193   4500008004100000245006100041210005800102260000900160300001400169490000600183520091700189653002601106653002101132653002601153653002401179100001401203700001401217856011701231       2009                        eng d00aQuery-by-multiple-examples using support vector machines0 aQuerybymultipleexamples using support vector machines  c2009  a202 - 2100 v73 aWe identify and explore an Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information need is described not by a set of terms but by a set of documents. Intuitive ideas for QBME include using the centroid of these documents or the well-known Rocchio algorithm to construct the query vector. We consider this problem from the perspective of text classification, and find that a better query vector can be obtained through learning with Support Vector Machines (SVMs). For online queries, we show how SVMs can be learned from one-class examples in linear time. For offline queries, we show how SVMs can be learned from positive and unlabeled examples together in linear or polynomial time, optimising some meaningful multivariate performance measures. The effectiveness and efficiency of the proposed approaches have been confirmed by our experiments on four real-world datasets.10aInformation retrieval10aMachine learning10aSupportvector machine10aText classification1 aZhang, D.1 aLee, W.S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-77953247724&partnerID=40&md5=8fea0823a5d70e478bf228f315a5ef8901994nas a2200181   4500008004100000245006700041210006600108260000900174300001400183490000600197520137800203653002101581653004601602653001801648653001201666100001701678856011701695       2007                        eng d00aPersonalized searching by learning wordnet-based user profiles0 aPersonalized searching by learning wordnetbased user profiles  c2007  a309 - 3220 v53 aThe amount of information available on the Web and in Digital Libraries is increasing over time. In this context, the role of user modeling and personalized information access is becoming crucial: Users need a personalized support in sifting through large amounts of retrieved information according to their interests. Information filtering and retrieval systems relying on this idea adapt their behavior to individual users by learning their preferences during the interaction in order to construct a profile of the user that can be later exploited in the search process. We propose a novel technique to learn user profiles which exploits word sense disambiguation based on the WordNet lexical database, in an attempt to produce semantic user profiles that might discover topics semantically closer to the user interests. Semantic profiles are used in the definition of a retrieval model that turns the traditional document-query search paradigm into a novel document-query-profile paradigm. As an example of this paradigm, we present an extension of the vector space model in which profiles are used to modify the ranking of search results obtained in response to a query, hopefully putting personally relevant items on the top of the result list. Experimental results in a movie retrieval scenario indicate that the proposed model to personalize Web search is effective.10aMachine learning10aPersonalization and information filtering10aUser modeling10aWordNet1 aSemeraro, G.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-66149191527&partnerID=40&md5=750d800edd5f7fe76d849dd465dafff9