We describe a new semantic relatedness measure combining the Wikipedia-based Explicit Semantic Analysis measure, the WordNet path measure and the mixed collocation index. Our measure achieves the currently highest results on the WS-353 test: a Spearman ρ coefficient of 0.79 (vs. 0.75 in (Gabrilovich and Markovitch, 2007)) when applying the measure directly, and a value of 0.87 (vs. 0.78 in (Agi...

This is the Lump team participation at SemEval 2017 Task 1 on Semantic Textual Similarity. Our supervised model relies on features which are multilingual or interlingual in nature. We include lexical similarities, cross-language explicit semantic analysis, internal representations of multilingual neural networks and interlingual word embeddings. Our representations allow to use large datasets i...

Explicit Semantic Analysis (ESA) has been recently proposed as an approach to computing semantic relatedness between words (and indirectly also between texts) and has thus a natural application in information retrieval, showing the potential to alleviate the vocabulary mismatch problem inherent in standard Bag-of-Word models. The ESA model has been also recently extended to cross-lingual retrie...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

Computing semantic relatedness of natural language texts is a key component of tasks such as information retrieval and summarization, and often depends on knowledge from a broad range of real-world concepts and relationships. We address this knowledge integration issue with a method of computing semantic relatedness using personalized PageRank (random walks) on a graph derived from Wikipedia. T...

RÉSUMÉ Nous présentons une extension du procédé d’analyse sémantique explicite de Gabrilovich et Markovitch. À l’aide de leur mesure de parenté sémantique, nous pondérons le graphe des catégories de Wikipédia. Puis, nous en extrayons un arbre couvrant minimal par le biais de l’algorithme de Chu-Liu & Edmonds. Nous définissons une notion de tfidf stratifié, les strates étant, pour une page Wikip...

Document clustering recently became a vital approach as numbers of documents on web and on proprietary repositories are increased in unprecedented manner. The documents that are written in human language generally contain some context and usage of words mainly dependent upon the same context; recently researchers have attempted to enrich document representation via external knowledge base. This...

Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weight...

In this paper, we describe our system submitted for the Sentiment Analysis task at SemEval 2013 (Task 2). We implemented a combination of Explicit Semantic Analysis (ESA) with Naive Bayes classifier. ESA represents text as a high dimensional vector of explicitly defined topics, following the distributional semantic model. This approach is novel in the sense that ESA has not been used for Sentim...