Semantic similarity

About: Semantic similarity is a research topic. Over the lifetime, 14605 publications have been published within this topic receiving 364659 citations. The topic is also known as: semantic relatedness.

Using Wikipedia and Wiktionary in domain-specific information retrieval

Abstract: The main objective of our experiments in the domain-specific track at CLEF 2008 is utilizing semantic knowledge from collaborative knowledge bases such as Wikipedia and Wiktionary to improve the effectiveness of information retrieval. While Wikipedia has already been used in IR, the application of Wiktionary in this task is new. We evaluate two retrieval models, i.e. SR-Text and SR-Word, based on semantic relatedness by comparing their performance to a statistical model as implemented by Lucene. We refer to Wikipedia article titles and Wiktionary word entries as concepts and map query and document terms to concept vectors which are then used to compute the document relevance. In the bilingual task, we translate the English topics into the document language, i.e. German, by using machine translation. For SR-Text, we alternatively perform the translation process by using cross-language links in Wikipedia, whereby the terms are directly mapped to concept vectors in the target language. The evaluation shows that the latter approach especially improves the retrieval performance in cases where the machine translation system incorrectly translates query terms.

Phonological Influences on Lexical (Mis)Selection

Abstract: Speakers produce words to convey meaning, but does meaning alone determine which words they say? We report three experiments that show independent semantic and phonological influences converging to determine word selection. Speakers named pictures (e.g., of a priest) following visually presented cloze sentences that primed either semantic competitors of the target object name (“The woman went to the convent to become a …”), homophones of the competitors (“I thought that there would still be some cookies left, but there were …”), or matched unrelated control object names. Primed semantic competitors (nun) were produced instead of picture names more often than primed unrelated control object names, showing the well-documented influence of semantic similarity on lexical selection. Surprisingly, primed homophone competitors (none) also substituted for picture names more often than control object names even though they only sounded like competitors. Thus, independent semantic and phonological influences can co...

Measurement of Text Similarity: A Survey

Abstract: Text similarity measurement is the basis of natural language processing tasks, which play an important role in information retrieval, automatic question answering, machine translation, dialogue systems, and document matching. This paper systematically combs the research status of similarity measurement, analyzes the advantages and disadvantages of current methods, develops a more comprehensive classification description system of text similarity measurement algorithms, and summarizes the future development direction. With the aim of providing reference for related research and application, the text similarity measurement method is described by two aspects: text distance and text representation. The text distance can be divided into length distance, distribution distance, and semantic distance; text representation is divided into string-based, corpus-based, single-semantic text, multi-semantic text, and graph-structure-based representation. Finally, the development of text similarity is also summarized in the discussion section.

Using Information Content to Evaluate Semantic Similarity on HowNet

Abstract: Evaluating Semantic similarity has a widely application areas range from Psychology, Linguistics, Cognitive Science to Artificial Intelligence. This paper proposes the merely use of HowNet to evaluate Information Content (IC) as the semantic similarity of two terms or word senses. While the conventional ways of measuring the IC of word senses must depend on both an ontology like WordNet and a large corpus, experiments of this paper prove that the semantic similarity measured in this method is easy to calculate and more approach to human judgments.

Ontology Driven Semantic Search

Abstract: The introduction of semantics on the web will lead to a new generation of services based on content rather than on syntax. Search engines will provide topic-based searches, retrieving resources conceptually related to the user informational need. Queries will be expressed in several ways, and will be mapped on the semantic level defining topics that must be retrieved from the web. Moving towards this new Web era, effective semantic search engines will provide means for successful searches avoiding the heavy burden experimented by users in a classical query-string based search task. In this paper we propose a search engine based on web resource semantics. Resources to be retrieved are semantically annotated using an existing open semantic elaboration platform and an ontology is used to describe the knowledge domain into which perform queries. Ontology navigation provides semantic level reasoning in order to retrieve meaningful resources with respect to a given information request.