Extended Boolean information retrieval

By Gerard A. Salton, Edward A. Fox, Harry Wu. 1983

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “The authors show how this technique oﬀers better performance than the classic TF-IDF [133] or LSH.”
The Fact Extraction and VERification (FEVER) Shared Task (2018, methodology): “Other teams report scoring based on sentence similarity using Word Mover’s Distance (Kusner et al., 2015) or cosine similarity over smooth inverse frequency weightings (Arora et al., 2017), ELMo embeddings (Peters et al., 2018) and TF-IDF (Salton et al., 1983).”
Improving Decision Analytics with Deep Learning: the Case of Financial Disclosures (2015, background): “The actual values are weighted [33] by the term frequency-inverse document frequency (tf-idf).”
Domain Control for Neural Machine Translation (2016, methodology): “Hence, before translation, the domain tag is automatically detected using an in-house domain classification module based on basic TF-IDF (Salton et al., 1983) technology to disambiguate between the six different domains.”
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums (2015, methodology): “In an IR system the vector for each document is compared with the one generated by a query, usually through a common technique known as TF/IDF (Term Frequency/Inverse Document Frequency, [8]) and a similarity metric such as the cosine distance [9].”
Spark: top-k keyword query in relational databases (2007, methodology): “Our proposed completeness factor is derived from the extended Boolean model [28].”
Efficient query evaluation using a two-level retrieval process (2003, result): “This continuity is comparable in nature to the p-norm model [19] that intermediates be-tween Boolean and vector-space processing models.”
Exploring social annotations for the semantic web (2006, methodology): “The probability of ’and’ query and ’or’ query can be calculated in (17) and (18) respectively using [28].”
Information filtering and information retrieval: two sides of the same coin? (1992, background): “Similarly, excluding documents that do not precisely match a query specification results in lower effectiveness [21, 30].”
Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “The Boolean Inexact Model BIM [301,302] bypasses this problem by providing a scoring mechanism so that the rule with the highest score classifies the document.”