A vector space model for automatic indexing

By Gerard A. Salton, Anita Wong, Chung-Shu Yang. 1975

Subfields: Information Retrieval & Search

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Citations in the corpus

Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring (2019, methodology): “Such methods include vector space models (Salton et al., 1975), LSI (Deerwester et al., 1990), supervised embeddings (Bai et al., 2009; Wu et al., 2018) and classical siamese networks (Bromley et al., 1994).”
From Frequency to Meaning: Vector Space Models of Semantics (2010, methodology): “The core idea is, given a query, rank the documents in order of decreasing cosine of the angles between the query vector and the document vectors (Salton et al., 1975).”
Multi-Relational Latent Semantic Analysis (2013, background): “In this representation, each text object can be represented by a high-dimensional sparse vector, such as a term-vector or a document-vector that denotes the statistics of term occurrences (Salton et al., 1975) in a large corpus.”
"What is relevant in a text document?": An interpretable machine learning approach (2016, methodology): “For several text applications, machine learning (ML) models based on global word statistics like TFIDF [1,2] or linear classiﬁers are known to perform remarkably well, e.g. for unsupervised keyword extraction [3] or document classiﬁcation [4].”
code2vec: learning distributed representations of code (2018, background): “The idea of distributed representations of words date back to Deer-wester et al. [1990] and even Salton et al. [1975], and are commonly based on the distributional hypothesis of Harris [1954] and Firth [1957], which states that words in similar contexts have similar meaning.”
A Structured Vector Space Model for Word Meaning in Context (2008, methodology): “We will explore the usability of vector space models of word meaning in NLP applications, formulated as the question of how to perform inferences on them in the context of the Textual Entailment task (Dagan et al., 2006).”
A Survey on Metric Learning for Feature Vectors and Structured Data (2013, background): “Bags-of(-visual)-words (Salton et al., 1975; Li and Perona, 2005) are a common example of such data.”
TakeLab: Systems for Measuring Semantic Text Similarity (2012, background): “Research predominantly focused either on the document similarity (Salton et al., 1975; Maguitman et al., 2005) or the word similarity (Budanitsky and Hirst, 2006; Agirre et al., 2009).”
Vector-based Models of Semantic Composition (2008, background): “…include automatic thesaurus extraction (Grefenstette, 1994), word sense discrimination (Sch¨utze, 1998) and disambiguation (McCarthy et al., 2004), collocation extraction (Schone and Jurafsky, 2001), text segmentation (Choi et al., 2001) , and notably information retrieval (Salton et al., 1975).”
Measuring Similarity between Business Process Models (2008, methodology): “The similarity between a query and a document is then calculated based on their vector representation as the cosine of the angle between the two vectors [2, 28].”
Modeling and solving term mismatch for full-text retrieval (2012, background): “The research community switched to ranked retrieval much earlier from the 1970s and early 1980s (Salton et al. 1975; Robertson and Spärck Jones 1976).”
Context-based search for 3D models (2010, methodology): “One way of comparing two sets of tags is the Vector Space Model [Salton et al. 1975]; we use a simpler approach that looks at the relative overlap of tags.”
A web-based kernel function for measuring the similarity of short text snippets (2006, methodology): “Directly applying traditional document similarity measures, such as the widely used cosine coefficient [17, 16], to”
Learning to rank code examples for code search engines (2017, methodology): “Then we compute the textual similarity between a query and a candidate answer using cosine similarity (Salton et al. 1975) as defined in (3).”
Artificial intelligence in recommender systems (2020, methodology): “One of the most commonly used retrieval techniques in content-based recommender systems is a keyword-based model known as the vector space model with term frequency-inverse document frequency weighting [8].”
Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach (2011, methodology): “In order to solve this problem, the term frequencies are transformed by TF-IDF (Term Frequency-Inverse Document Frequency, here, document refers to review) weighting [25].”