[PDF] Top 20 Domain Kernels for Text Categorization

... the Domain Kernel allows us to re- duce drastically the amount of training data required for ...the Domain Kernel takes into account both variability and ambiguity, being able to estimate similarity even ... See full document

... of Domain Models allows us to reduce the amount of training data, opening an interesting research di- rection for all those NLP tasks for which the Knowl- edge Acquisition Bottleneck is a crucial ...to Text ... See full document

... for text cate- ...(2) domain mismatch between the training corpus for a dis- course parser and the domain where the discourse parser is ...ing domain adaptation methods or even direct dis- ... See full document

... of text categorization finds applications in a wide variety of domains, from news filter- ing and document organization to opinion mining and spam ...classifying text in a timely ... See full document

... non-linear inner product functions (i.e., kernels) and introducing soft-margins similarly to SVMs (see [1]). It will be of particular interest to test its perfor- mance on the categories with very few positive ... See full document

... Weight-sharing in NNs. Recent work has con- sidered stochastically sharing weights in neural models. Notably, Chen et al. (2015). proposed randomly sharing weights in neural networks. Elsewhere, Han et al. (2015) ... See full document

... automatic text cat- egorization systems, which rely on data- intensive models induced from large train- ing data, our automatic text categorization tool applies data-independent classifiers: a ... See full document

... get domain to extract some relevant features that may reduce the difference between the domains (Blitzer et ...the text classification task (Raina et ...given text classi- fication task (Raina et ... See full document

... check, domain mapping and knowledge base management (ii) Lexical Analysis like identifying token type and checking for attribute token (iii) Syntactic Analysis using Stanford Parser and Multi-Liaison algorithm, ... See full document

... In this section, we present statistical regularizers, i. e. with groups of words based on co-occurrences, as opposed to syntactic ones (Mitra et al., 1997). Network of features Sandler et al. (2009) intro- duced ... See full document

... In Question Answering, the latter problem is mostly tackled by using different heuristics and classifiers, which aim at extracting the best an- swers (Chen et al., 2006; Collins-Thompson et al., 2004). However, for ... See full document

... analyzing text, comments and behavior of the ...behaviors. Text categorization is a wide domain to make machine able to understand and organize the document (text, document), as a part ... See full document

... recurring text collections with several unsupervised and supervised weighting schemes, we show that the ones we propose generally perform better than or comparably to other ones in terms of accuracy, using two ... See full document

... As an instance-based classification method, k-NN has been known as an effective approach to a broad range of pattern recognition and text classification problems (Duda et al., 2001; Yang, 1994). In k-NN algorithm, ... See full document

... automatic text categorization has diversified from newswire articles and web pages to electronic mails and newsgroup postings, it is a difficult task to create training data for each application area (Nigam ... See full document

... retrieval, text categorization has recently become an active research ...of text categorization is to allot entrics from a set of prespecified categories to a ...a text ... See full document

... Chinese text (as well as other Asian languages) has a very large term set and a satisfying subset is usually not smaller than 50000 (Li et ...English text categorization (Yang and Pedersen, 1997; ... See full document

... The key to getting good recall and precision on texts such as these is to consider the weights of the individual words and phrases to determine what industries could be involved, but to [r] ... See full document

... In this paper, we show how linguistic informa- tion of various kinds—parse trees, thematic topics, and hierarchical word clusterings—can be used to construct group lasso variants that impose linguis- tic bias without ... See full document

... In this paper the enhanced features are used to fin distribution of a word in the document. The novel values assigned to a word are called features. These features like compactness of the appearances of the word and the ... See full document