@article {333,
	title = {Word combination kernel for text categorization},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {202 - 211},
	abstract = {We 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.},
	keywords = {Kernel methods, Machine learning, Support vector machine, Text classification, Word-combination kernel},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932\&partnerID=40\&md5=8649dfc8c1ee23ca70f2b137a74a19e4},
	author = {Zhang, L. and Hu, X. and Qin, S.}
}
@article {290,
	title = {Detecting human sentiment from text using a proximity-based approach},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2011},
	pages = {206 - 212},
	abstract = {Sentiment 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.},
	keywords = {Machine learning, Movie reviews, Proximity analysis, Sentiment analysis, Text mining},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855397249\&partnerID=40\&md5=a009c46b7b54e127e88ca882940b9767},
	author = {Hasan, S.M.S. and Adjeroh, D.A.}
}
@article {245,
	title = {Enabling automatic just-in-time evaluation of in-class discussions in on-line collaborative learning practices},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2010},
	pages = {322 - 329},
	abstract = {Learning 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.},
	keywords = {Automatic evaluation, Collaborative learning, Groupware, Knowledge discovery, Machine learning},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960216812\&partnerID=40\&md5=0f38528ca7e6fa26bdcaf9c0b81206c9},
	author = {Caball{\'e}, S. and Lapedriza, {\`A}. and Masip, D. and Xhafa, F. and Abraham, A.}
}
@article {194,
	title = {Enabling automatic just-in-time evaluation of in-class discussions in on-line collaborative learning practices},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Cited By (since 1996):3Export Date: 10 July 2014},
	month = {2009},
	pages = {289 - 296},
	abstract = {Learning 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.},
	keywords = {Automatic evaluation, Collaborative learning, Groupware, Knowledge discovery, Machine learning},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953238102\&partnerID=40\&md5=6dce6350dd861170e2133b9b2f597627},
	author = {Caball{\'e}, S. and Lapedriza, {\`A}. and Masip, D. and Xhafa, F. and Abraham, A.}
}
@article {188,
	title = {Query-by-multiple-examples using support vector machines},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2009},
	pages = {202 - 210},
	abstract = {We 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.},
	keywords = {Information retrieval, Machine learning, Supportvector machine, Text classification},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953247724\&partnerID=40\&md5=8fea0823a5d70e478bf228f315a5ef89},
	author = {Zhang, D. and Lee, W.S.}
}
@article {101,
	title = {Personalized searching by learning wordnet-based user profiles},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2007},
	pages = {309 - 322},
	abstract = {The 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.},
	keywords = {Machine learning, Personalization and information filtering, User modeling, WordNet},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-66149191527\&partnerID=40\&md5=750d800edd5f7fe76d849dd465dafff9},
	author = {Semeraro, G.}
}
