Semantic text similarity using corpus-based word similarity and string similarity

By Aminul Islam, Diana Inkpen. ACM Transactions on Knowledge Discovery from Data, 2008

560 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7038ac4463ea0e1ac08dfb7e889cbd599693cdc3

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In Artificial Intelligence

Citations in the corpus

Future Data and Security Engineering (2014, methodology): “The CryptDB scheme [11] defines layers of encryption for different types of database queries.”
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity (2013, methodology): “Most similar to our approach are the methods of Islam and Inkpen (2008) and Corley and Mihalcea (2005), who performed a word-to-word similarity alignment; however, they did not operate at the sense level.”
Short Text Similarity with Word Embeddings (2015, methodology): “, in terms of edit distance [31], lexical overlap [23] or largest common substring [21].”
DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition (2015, background): “Early work on sentence similarity (Corley and Mi-halcea, 2005; Mihalcea et al., 2006; Li et al., 2006; Islam and Inkpen, 2008) established the basic procedural framework under which most modern algorithms operate: computing sentence similarity as a mean of word similarities across the two input…”
TakeLab: Systems for Measuring Semantic Text Similarity (2012, background): “Evaluating the similarity of short texts such as sentences or paragraphs (Islam and Inkpen, 2008; Mihalcea et al., 2006; Oliva et al., 2011) received less attention from the research community.”
Modeling Sentences in the Latent Space (2012, background): “…is not a problem per se, however the issue is that it is very strict in its assignment of a positive label, for example the following sentence pair as cited in (Islam and Inkpen, 2008) is rated not semantically similar: Ballmer has been vocal in the past warning that Linux is a threat to Microsoft.”
Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words in a Latent Variable Model (2013, background): “Early work in SS focused on word pair similarity in the high dimensional space (Li et al., 2006; Liu et al., 2007; Islam and Inkpen, 2008; Tsatsaronis et al., 2010; Ho et al., 2010), where co-occurrence information was not efficiently exploited.”