Incorporating semantic features from the WordNet lexical database is among one of the many approaches that have been tried to improve the predictive performance of text classification models. The intuition behind this is that keywords in the training set alone may not be extensive enough to enable generation of a universal model for a category, but if we incorporate the word relationships in WordNet, a more accurate model may be possible. Other researchers have previously evaluated the effectiveness of incorporating WordNet synonyms, hypernyms, and hyponyms into text classification models. Generally, they have found that improvements in accuracy using features derived from these relationships are dependent upon the nature of the text corpora from which the document collections are extracted. In this paper, we not only reconsider the role of WordNet synonyms, hypernyms, and hyponyms in text classification models, we also consider the role of WordNet meronyms and holonyms. Incorporating these WordNet relationships into a Coordinate Matching classifier, a Naive Bayes classifier, and a Support Vector Machine classifier, we evaluate our approach on six document collections extracted from the Reuters-21578, USENET, and Digi-Trad text corpora. Experimental results show that none of the WordNet relationships were effective at increasing the accuracy of the Naive Bayes classifier. Synonyms, hypernyms, and holonyms were effective at increasing the accuracy of the Coordinate Matching classifier, and hypernyms were effective at increasing the accuracy of the SVM classifier.
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network.
George, Clint Pazhayidam (University of Florida) | Puri, Sahil (University of Florida) | Wang, Daisy Zhe (University of Florida) | Wilson, Joseph N. (University of Florida) | Hamilton, William F. (University of Florida)
Electronic discovery is an interesting subproblem of information retrieval in which one identifies documents that are potentially relevant to issues and facts of a legal case from an electronically stored document collection (a corpus). In this paper, we consider representing documents in a topic space using the well-known topic models such as latent Dirichlet allocation and latent semantic indexing, and solving the information retrieval problem via finding document similarities in the topic space rather doing it in the corpus vocabulary space. We also develop an iterative SMART ranking and categorization framework including human-in-the-loop to label a set of seed (training) documents and using them to build a semi-supervised binary document classification model based on Support Vector Machines. To improve this model, we propose a method for choosing seed documents from the whole population via an active learning strategy. We report the results of our experiments on a real dataset in the electronic discovery domain.
Text clustering is a widely used techniques to automatically draw out patterns from a set of documents. This notion can be extended to customer segmentation in the digital marketing field. As one of its main core is to understand what drives visitors to come, leave and behave on site. One simple way to do this is by reviewing words that they used to arrive on site and what words they used ( what things they searched) once they're on your site. Another usage of text clustering is for document organization or indexing (tagging).