The Explicit Semantic Analysis (ESA) model based on term cooccurrences in Wikipedia has been regarded as state-of-the-art semantic relatedness measure in the recent years. We provide an analysis of the important parameters of ESA using datasets in five different languages. Additionally, we propose the use of ESA with multiple lexical semantic resources thus exploiting multiple evidence of term ...

We present a multi-feature system for computing the semantic similarity between two sentences. We introduce the use of soft alignment for computing text similarity, and also evaluate different methods to produce it. The main features used by our system are based on alignment and Explicit Semantic Analysis. Our system was above the median scores for 4 out of the 5 datasets at SemEval 2016 STS Ta...

This paper describes the methods used in the submission of Knowledge Media institute (KMI), The Open University to the NTCIR-9 Cross-Lingual Link Discovery (CLLD) task entitled CrossLink. KMI submitted four runs for link discovery from English to Chinese; however, the developed methods, which utilise Explicit Semantic Analysis (ESA), are applicable also to other language combinations. Three of ...

With the rapid growth of data generated by social web applications new paradigms in the generation of knowledge are opening. This paper introduces Crowd Explicit Sentiment Analysis (CESA) as an approach for sentiment analysis in social media environments. Similar to Explicit Semantic Analysis, microblog posts are indexed by a predefined collection of documents. In CESA, these documents are buil...

In this paper we discuss our participation to the 2013 Semeval Semantic Textual Similarity task. Our core features include (i) a set of metrics borrowed from automatic machine translation, originally intended to evaluate automatic against reference translations and (ii) an instance of explicit semantic analysis, built upon opening paragraphs of Wikipedia 2010 articles. Our similarity estimator ...

We present an extended, thematically reinforced version of Gabrilovich and Markovitch’s Explicit Semantic Analysis (ESA), where we obtain thematic information through the category structure of Wikipedia. For this we first define a notion of categorical tfidf which measures the relevance of terms in categories. Using this measure as a weight we calculate a maximal spanning tree of the Wikipedia ...

Explicit Semantic Analysis (ESA) utilizes the Wikipedia knowledge base to represent the semantics of a word by a vector where every dimension refers to an explicitly defined concept like a Wikipedia article. ESA inherently assumes that Wikipedia concepts are orthogonal to each other, therefore, it considers that two words are related only if they co-occur in the same articles. However, two word...

In recent years supervised representation learning has provided state of the art or close to the state of the art results in semantic analysis tasks including ranking and information retrieval. The core idea is to learn how to embed items into a latent space such that they optimize a supervised objective in that latent space. The dimensions of the latent space have no clear semantics, and this ...

In this paper, we describe our query expansion approach submitted for the Semantic Enrichment task in Cultural Heritage in CLEF (CHiC) 2012. Our approach makes use of an external knowledge base such as Wikipedia and DBpedia. It consists of two major steps, concept candidates generation from knowledge bases and the selection of K-best related concepts. For selecting the K-best concepts, we ranke...