A web-based kernel function for measuring the similarity of short text snippets

By Mehran Sahami, Tim Heilman. The Web Conference, 2006

820 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3f755b0333440f1346a8ba889e404c024cfb21e0

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In Artificial Intelligence

Citations in the corpus

A word at a time: computing word relatedness using temporal semantic analysis (2011, background): “Some works [30, 26] proposed to use the Web as a source of additional knowledge for measuring similarity of short text snippets.”
A biterm topic model for short texts (2013, background): “For example, Sahami et al.[28] suggested a search-snippet-based similarity measure for short texts.”
Learning to classify short and sparse text & web with hidden topics from large-scale data collections (2008, methodology): “Sahami & Heilman 2006 [ 30 ] also measure the relatedness between text snippets by using search engines and a similarity kernel function.”
A Survey of Text Clustering Algorithms (2012, methodology): “A specific technique, which leverages a search engine to expand text representation , was proposed in [79].”
Efficient parallel set-similarity joins using MapReduce (2010, background): “A list of possible applications includes: detecting near duplicate web-pages in web crawling [14], document clustering[5], plagiarism detection [15], master data management, making recommendations to users based on their similarity to other users in query refinement [22], mining in social networking”
Measuring Word Relatedness Using Heterogeneous Vector Space Models (2012, methodology): “Web -based VSMs leverage Web search results to form a vector of each query (Sahami and Heilman, 2006).”
Fast and Accurate Annotation of Short Texts with Wikipedia Pages (2010, background): “One approach consists of extending the classic term-based vector-space model with additional dimensions corresponding to features (concepts) extracted from an external knowledge base, such as DMOZ [4, 6], Wikipedia [7, 1, 9], or even the whole Web (such as the Google’s kernel [15]).”
Scaling Up All Pairs Similarity Search (background): “Cosine similarity is widely used, and produces high quality results across several domains [8] [9] [19] [20].”
A Multi-Channel Deep Neural Network for Relation Extraction (2020, methodology): “Based on parsing tree, tree kernel methods are widely used to extract information from sentences [25], [26].”
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems (2013, methodology): “A variation on this approach leverages web search results (e.g., snip-pets) to provide context for the short texts and enrich their vectors using the words in the snippets (Sahami and Heilman, 2006).”