Sentence similarity based on semantic nets and corpus statistics
Open accessAbstract
Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database and from corpus statistics. The use of a lexical database enables our method to model human common sense knowledge and the incorporation of corpus statistics allows our method to be adaptable to different domains. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition
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Fingerprint and hash-based are common heuristic retrieval models.
“Fingerprint [115], [116] and hash-based [117], [118] are common heuristic retrieval models”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [116]
SEM and FUZZY methods incorporate different semantic features, thesaurus dictionaries, and lexical databases, such as WordNet.
“On the other hand, SEM [14], [15] and FUZZY [16]-[19] methods incorporate different semantic features, thesaurus dictionaries, and lexical databases, such as WordNet.”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [14]
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- Authors
- Li, Y., McLean, D., Bandar, Z. A., O'Shea, J. D., & Crockett, K.
- Affiliations
- University of Ulster (GB)
- Manchester Metropolitan University (GB)
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2006-08
- Volume
- 18
- Issue
- 8
- Pages
- 1138-1150
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2121184547
- Cited by
- 819
- References
- 39
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 19 · 2023: 25 · 2022: 26 · 2021: 51 · 2020: 51 · 2019: 79 · 2018: 72 · 2017: 68 · 2016: 58 · 2015: 70 · 2014: 59 · 2013: 49 · 2012: 55
- FWCI
- 17.88
- Type
- journal article
- Language
- en
- Open access
- green
- usir.salford.ac.uk
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- University of Salford Institutional Repository (University of Salford) (submittedVersion, cc-by)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- University of Salford Manchester
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- sentence similarity, semantic networks, corpus statistics, lexical databases, word order, commonsense knowledge, domain adaptation, short text similarity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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