From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

An information retrieval approach to ontology mapping

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud \u

Verb similarity: comparing corpus and psycholinguistic data

Similarity, which plays a key role in fields like cognitive science, psycholinguistics and natural language processing, is a broad and multifaceted concept. In this work we analyse how two approaches that belong to different perspectives, the corpus view and the psycholinguistic view, articulate similarity between verb senses in Spanish. Specifically, we compare the similarity between verb senses based on their argument structure, which is captured through semantic roles, with their similarity defined by word associations. We address the question of whether verb argument structure, which reflects the expression of the events, and word associations, which are related to the speakers' organization of the mental lexicon, shape similarity between verbs in a congruent manner, a topic which has not been explored previously. While we find significant correlations between verb sense similarities obtained from these two approaches, our findings also highlight some discrepancies between them and the importance of the degree of abstraction of the corpus annotation and psycholinguistic representations.La similitud, que desempeña un papel clave en campos como la ciencia cognitiva, la psicolingüística y el procesamiento del lenguaje natural, es un concepto amplio y multifacético. En este trabajo analizamos cómo dos enfoques que pertenecen a diferentes perspectivas, la visión del corpus y la visión psicolingüística, articulan la semejanza entre los sentidos verbales en español. Específicamente, comparamos la similitud entre los sentidos verbales basados en su estructura argumental, que se capta a través de roles semánticos, con su similitud definida por las asociaciones de palabras. Abordamos la cuestión de si la estructura del argumento verbal, que refleja la expresión de los acontecimientos, y las asociaciones de palabras, que están relacionadas con la organización de los hablantes del léxico mental, forman similitud entre los verbos de una manera congruente, un tema que no ha sido explorado previamente. Mientras que encontramos correlaciones significativas entre las similitudes de los sentidos verbales obtenidas de estos dos enfoques, nuestros hallazgos también resaltan algunas discrepancias entre ellos y la importancia del grado de abstracción de la anotación del corpus y las representaciones psicolingüísticas.La similitud, que exerceix un paper clau en camps com la ciència cognitiva, la psicolingüística i el processament del llenguatge natural, és un concepte ampli i multifacètic. En aquest treball analitzem com dos enfocaments que pertanyen a diferents perspectives, la visió del corpus i la visió psicolingüística, articulen la semblança entre els sentits verbals en espanyol. Específicament, comparem la similitud entre els sentits verbals basats en la seva estructura argumental, que es capta a través de rols semàntics, amb la seva similitud definida per les associacions de paraules. Abordem la qüestió de si l'estructura de l'argument verbal, que reflecteix l'expressió dels esdeveniments, i les associacions de paraules, que estan relacionades amb l'organització dels parlants del lèxic mental, formen similitud entre els verbs d'una manera congruent, un tema que no ha estat explorat prèviament. Mentre que trobem correlacions significatives entre les similituds dels sentits verbals obtingudes d'aquests dos enfocaments, les nostres troballes també ressalten algunes discrepàncies entre ells i la importància del grau d'abstracció de l'anotació del corpus i les representacions psicolingüístiques

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015