Topic
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.
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Proceedings Article•
11 Jul 2010TL;DR: This work formally defines the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers and presents methods that automatically identify these constituents as well as their semantic roles based on Markov and semi-Markov conditional random fields.
Abstract: Determining the semantic intent of web queries not only involves identifying their semantic class, which is a primary focus of previous works, but also understanding their semantic structure. In this work, we formally define the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers. We present methods that automatically identify these constituents as well as their semantic roles based on Markov and semi-Markov conditional random fields. We show that the use of semantic features and syntactic features significantly contribute to improving the understanding performance.
95 citations
TL;DR: A framework for creating reference standards for functional testing of computerized measures of semantic relatedness is developed and it is shown that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers.
95 citations
95 citations
Proceedings Article•
01 Jan 2008TL;DR: In this cross-lingual extension of ESA, the cross-language links of Wikipedia are used in order to map the ESA vectors between different languages, thus allowing retrieval across languages.
Abstract: We have participated on the monolingual and bilingual CLEF Ad-Hoc Retrieval Tasks, using a novel extension of the by now well-known Explicit Semantic Analysis (ESA) approach. We call this extension Cross-Language Explicit Semantic Analysis (CL-ESA) as it allows to apply ESA in a cross-lingual information retrieval setting. In essence, ESA represents documents as vectors in the space of Wikipedia articles, using the tfidf measure to capture how “important” a Wikipedia article is for a specific word. The interesting property of ESA is that arbitrary documents can be represented as a vector with respect to the Wikipedia article space. ESA thus replaces the standard BOW model for retrieval. In our cross-lingual extension of ESA, the cross-language links of Wikipedia are used in order to map the ESA vectors between different languages, thus allowing retrieval across languages. Our results are far behind the ones of other systems on the monolingual and ad-hoc retrieval tasks, but our motivation was to find out the potential of the CL-ESA approach using a first and unoptimized implementation thereof.
95 citations
TL;DR: The article aims to throw some light on the term semantic preference, and to examine in more detail some aspects of semantic preference that are frequently neglected in research.
Abstract: InthispaperIwanttore-examinethekeycorpus-linguisticnotionofsemantic preference.This is definedhere as the collocation ofa lexical item with items from a specific (more or less general) semantic subset. The article aims to throw some light on the term semantic preference, and to examine in more detail some aspects of semantic preference that are frequently neglected in research. It also discusseshowsemanticpreference interacts with syntaxand meaning, and what happens when semantic preferences are not ‘realized’ in context. Finally, it seeks to illuminate the distinction between semantic preference and semantic prosody,and points to future research in this area.
95 citations