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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.


Papers
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Proceedings ArticleDOI
20 Apr 2009
TL;DR: Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall, and appears to be substantially more effective on noisy and multi-theme documents than existing methods.
Abstract: We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia.Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages.Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.

271 citations

Journal ArticleDOI
TL;DR: This paper examined how the cross-linguistic similarity of translation equivalents affects bilingual word recognition and found that cognates with varying degrees of form overlap between their English and Dutch counterparts showed a large discontinuous processing advantage and were subject to facilitation from phonological similarity.

271 citations

Proceedings ArticleDOI
12 Aug 2012
TL;DR: A large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process, and learns for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears.
Abstract: Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.

270 citations

Patent
29 Aug 2000
TL;DR: In this paper, a neural network is used to extract semantic profiles from text corpus and a new set of documents, such as world wide web pages obtained from the Internet, are then submitted for processing to the same neural network, which computes a semantic profile representation for these pages using the semantic relations learned from profiling the training documents.
Abstract: A process and system for database storage and retrieval are described along with methods for obtaining semantic profiles from a training text corpus, i.e., text of known relevance, a method for using the training to guide context-relevant document retrieval, and a method for limiting the range of documents that need to be searched after a query. A neural network is used to extract semantic profiles from text corpus. A new set of documents, such as world wide web pages obtained from the Internet, is then submitted for processing to the same neural network, which computes a semantic profile representation for these pages using the semantic relations learned from profiling the training documents. These semantic profiles are then organized into clusters in order to minimize the time required to answer a query. When a user queries the database, i.e., the set of documents, his or her query is similarly transformed into a semantic profile and compared with the semantic profiles of each cluster of documents. The query profile is then compared with each of the documents in that cluster. Documents with the closest weighted match to the query are returned as search results.

270 citations

Journal ArticleDOI

269 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023202
2022522
2021641
2020837
2019866
2018787