Thematic Annotation: extracting concepts out of documents

Contrarily to standard approaches to topic annotation, the technique used in this work does not centrally rely on some sort of -- possibly statistical -- keyword extraction. In fact, the proposed annotation algorithm uses a large scale semantic database -- the EDR Electronic Dictionary -- that provides a concept hierarchy based on hyponym and hypernym relations. This concept hierarchy is used to generate a synthetic representation of the document by aggregating the words present in topically homogeneous document segments into a set of concepts best preserving the document's content. This new extraction technique uses an unexplored approach to topic selection. Instead of using semantic similarity measures based on a semantic resource, the later is processed to extract the part of the conceptual hierarchy relevant to the document content. Then this conceptual hierarchy is searched to extract the most relevant set of concepts to represent the topics discussed in the document. Notice that this algorithm is able to extract generic concepts that are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure

From Review to Rating: Exploring Dependency Measures for Text Classification

Various text analysis techniques exist, which attempt to uncover unstructured information from text. In this work, we explore using statistical dependence measures for textual classification, representing text as word vectors. Student satisfaction scores on a 3-point scale and their free text comments written about university subjects are used as the dataset. We have compared two textual representations: a frequency word representation and term frequency relationship to word vectors, and found that word vectors provide a greater accuracy. However, these word vectors have a large number of features which aggravates the burden of computational complexity. Thus, we explored using a non-linear dependency measure for feature selection by maximizing the dependence between the text reviews and corresponding scores. Our quantitative and qualitative analysis on a student satisfaction dataset shows that our approach achieves comparable accuracy to the full feature vector, while being an order of magnitude faster in testing. These text analysis and feature reduction techniques can be used for other textual data applications such as sentiment analysis.Comment: 8 page

Wikipedia-based hybrid document representation for textual news classification

Automatic classification of news articles is a relevant problem due to the large amount of news generated every day, so it is crucial that these news are classified to allow for users to access to information of interest quickly and effectively. On the one hand, traditional classification systems represent documents as bag-of-words (BoW), which are oblivious to two problems of language: synonymy and polysemy. On the other hand, several authors propose the use of a bag-of-concepts (BoC) representation of documents, which tackles synonymy and polysemy. This paper shows the benefits of using a hybrid representation of documents to the classification of textual news, leveraging the advantages of both approaches-the traditional BoW representation and a BoC approach based on Wikipedia knowledge. To evaluate the proposal, we used three of the most relevant algorithms in the state-of-the art-SVM, Random Forest and Naïve Bayes-and two corpora: the Reuters-21578 corpus and a purpose-built corpus, Reuters-27000. Results obtained show that the performance of the classification algorithm depends on the dataset used, and also demonstrate that the enrichment of the BoW representation with the concepts extracted from documents through the semantic annotator adds useful information to the classifier and improves their performance. Experiments conducted show performance increases up to 4.12% when classifying the Reuters-21578 corpus with the SVM algorithm and up to 49.35% when classifying the corpus Reuters-27000 with the Random Forest algorithm.Atlantic Research Center for Information and Communication TechnologiesXunta de Galicia | Ref. R2014/034 (RedPlir)Xunta de Galicia | Ref. R2014/029 (TELGalicia