Similarity Measures for Short Segments of Text

By Donald Metzler, Susan T. Dumais, Christopher Meek. European Conference on Information Retrieval, 2007

341 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/68db32ceae99879d0e55fd37e4f0458cfc530499

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Citations in the corpus

Improving retrieval of short texts through document expansion (2012, background): “Familiar operations such as measuring inter-document similarity, scholars have found, is difficult given the brevity of many documents in collections of user-generated content [20].”
Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods (2010, methodology): “…years, the information retrieval community has extensively explored the task of query expansion by applying paraphrasing techniques to generate similar or related queries (Beeferman and Berger 2000; Jones et al. 2006; Sahami and Hellman 2006; Metzler, Dumais, and Meek 2007; Shi and Yang 2007).”
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.”
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems (2013, background): “, 2004), query reformulation (Metzler et al., 2007), automatic machine translation evaluation (Kauchak and Barzilay, 2006) and schema matching (Han et al.”
A Survey of Text Clustering Algorithms (2012, background): “A comparison of several simple measures for computing similarity of short text segments can be found in [66].”
An Introduction to Neural Information Retrieval (2018, neutral): “When computing similarity between pairs of short-texts, vocabulary mismatches are more likely than when the retrieved items contain long text descriptions [Metzler et al., 2007].”
Learning to classify short and sparse text & web with hidden topics from large-scale data collections (2008, background): “2007 [27] evaluated a wide range of similarity measures for short queries from Web search logs.”
Short Text Similarity with Word Embeddings (2015, background): “Being able to do so successfully is beneficial in many settings in information retrieval like search [26], query suggestion [30], automatic summarization [3] and image finding [12].”
The Evaluation of Sentence Similarity Measures (2008, background): “In [15], the effectiveness of lexical matching, language model, and hybrid measures, in computing the similarity between two short queries are investigated.”
Defining and Evaluating Fair Natural Language Generation (2020, background): “Basic similarity measures involve matching words or phrases in the surface representation of a text; in other words, these measures are purely lexical (Metzler et al., 2007).”
Identifying topical authorities in microblogs (2011, background): “Also, the restricted character length of microblogs does not lead to large variations in number of words per tweet, so a tf-idf [18] based normalization followed by cosine similarity would not be most effective [15].”