# Compound-term processing

> Mediated Wiki article. Canonical URL: https://mediated.wiki/source/Compound-term_processing
> Markdown URL: https://mediated.wiki/source/Compound-term_processing.md
> Source: https://en.wikipedia.org/wiki/Compound-term_processing
> Source revision: 997530676
> License: Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/)

**Compound-term processing,** in [information-retrieval](/source/Information_retrieval), is search result matching on the basis of [compound terms](/source/Compound_term). Compound terms are built by combining two or more simple terms; for example, "triple" is a single word term, but "triple heart bypass" is a compound term.

Compound-term processing is a new approach to an old problem: how can one improve the relevance of search results while maintaining ease of use? Using this technique, a search for *survival rates following a triple heart bypass in elderly people* will locate documents about this topic even if this precise phrase is not contained in any document. This can be performed by a [concept search](/source/Concept_search), which itself uses compound-term processing. This will extract the key concepts automatically (in this case "survival rates", "triple heart bypass" and "elderly people") and use these concepts to select the most relevant documents.

## Techniques

In August 2003, [Concept Searching Limited](/source/Concept_Searching_Limited) introduced the idea of using statistical compound-term processing.[1]

CLAMOUR is a European collaborative project which aims to find a better way to classify when collecting and disseminating industrial information and statistics. CLAMOUR appears to use a linguistic approach, rather than one based on [statistical modelling](/source/Statistical_model).[2]

## History

Techniques for probabilistic weighting of single word terms date back to at least 1976 in the landmark publication by [Stephen E. Robertson](/source/Stephen_Robertson_(computer_scientist)) and [Karen Spärck Jones](/source/Karen_Sp%C3%A4rck_Jones).[3] Robertson stated that the assumption of word independence is not justified and exists as a matter of mathematical convenience. His objection to the term independence is not a new idea, dating back to at least 1964 when H. H. Williams stated that "[t]he assumption of independence of words in a document is usually made as a matter of mathematical convenience".[4]

In 2004, Anna Lynn Patterson filed patents on "phrase-based searching in an information retrieval system"[5] to which [Google](/source/Google) subsequently acquired the rights.[6]

## Adaptability

Statistical compound-term processing is more adaptable than the process described by Patterson. Her process is targeted at searching the [World Wide Web](/source/World_Wide_Web) where an extensive statistical knowledge of common searches can be used to identify candidate phrases. Statistical compound term processing is more suited to [enterprise search](/source/Enterprise_search) applications where such [a priori](/source/A_priori_and_a_posteriori) knowledge is not available.

Statistical compound-term processing is also more adaptable than the linguistic approach taken by the CLAMOUR project, which must consider the syntactic properties of the terms (i.e. part of speech, gender, number, etc.) and their combinations. CLAMOUR is highly language-dependent, whereas the statistical approach is language-independent.

## Applications

Compound-term processing allows information-retrieval applications, such as [search engines](/source/Search_engines), to perform their matching on the basis of multi-word concepts, rather than on single words in isolation which can be highly ambiguous.

Early search engines looked for documents containing the words entered by the user into the search box . These are known as [keyword search](/source/Keyword_search) engines. [Boolean search](/source/Boolean_search) engines add a degree of sophistication by allowing the user to specify additional requirements. For example, "Tiger NEAR Woods AND (golf OR golfing) NOT Volkswagen" uses the operators "NEAR", "AND", "OR" and "NOT" to specify that these words must follow certain requirements. A [phrase search](/source/Phrase_search) is simpler to use, but requires that the exact phrase specified appear in the results.

## See also

- [Concept Searching Limited](/source/Concept_Searching_Limited)

- [Enterprise search](/source/Enterprise_search)

- [Information retrieval](/source/Information_retrieval)

## References

1. **[^](#cite_ref-1)** ["Lateral Thinking in Information Retrieval"](https://web.archive.org/web/20171115145846/https://www.conceptsearching.com/Web/UserFiles/File/Concept%20Searching%20Lateral%20Thinking.pdf) (PDF). *Information Management and Technology*. 36 PART 4. Archived from [the original](http://www.conceptsearching.com/Web/UserFiles/File/Concept%20Searching%20Lateral%20Thinking.pdf) (PDF) on 2017-11-15. Retrieved 2008-06-20. The British Library Direct catalogue entry can be found here:[\[1\]](http://direct.bl.uk/bld/PlaceOrder.do?UIN=138451913&ETOC=RN) [Archived](https://web.archive.org/web/20120210133832/http://direct.bl.uk/bld/PlaceOrder.do?UIN=138451913&ETOC=RN) 2012-02-10 at the [Wayback Machine](/source/Wayback_Machine)

1. **[^](#cite_ref-2)** [\[2\]](http://webarchive.nationalarchives.gov.uk/20040117000117/statistics.gov.uk/methods_quality/clamour/default.asp) National Statistics CLAMOUR project

1. **[^](#cite_ref-3)** [Robertson, S. E.](/source/Stephen_Robertson_(computer_scientist)); [Spärck Jones, K.](/source/Karen_Sp%C3%A4rck_Jones) (1976). "Relevance weighting of search terms". *Journal of the American Society for Information Science*. **27** (3): 129. [doi](/source/Doi_(identifier)):[10.1002/asi.4630270302](https://doi.org/10.1002%2Fasi.4630270302).

1. **[^](#cite_ref-4)** WILLIAMS, J.H. (1965). ["Results of classifying documents with multiple discriminant functions"](https://web.archive.org/web/20110717145048/http://oai.dtic.mil/oai/oai?verb=getRecord). *Statistical Association Methods for Mechanized Documentation, National Bureau of Standards*. Washington: 217–224. Archived from [the original](http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=AD0612272) on 2011-07-17. Retrieved 2015-05-21.

1. **[^](#cite_ref-5)** [US 20060031195](https://worldwide.espacenet.com/textdoc?DB=EPODOC&IDX=US20060031195)

1. **[^](#cite_ref-6)** [Google Acquires Cuil Patent Applications](http://www.seobythesea.com/2012/02/google-acquires-cuil-patent-applications/)

v t e Natural language processing General terms AI-complete Bag-of-words n-gram Bigram Trigram Computational linguistics Natural language understanding Stop words Text processing Text analysis Argument mining Collocation extraction Concept mining Coreference resolution Deep linguistic processing Distant reading Information extraction Knowledge extraction Logic translation Named-entity recognition Ontology learning Parsing semantic syntactic Part-of-speech tagging Semantic analysis Semantic role labeling Semantic decomposition Semantic similarity Sentiment analysis Stance detection Stylometry adversarial Terminology extraction Text mining Textual entailment Truecasing Word-sense disambiguation Word-sense induction Text segmentation Compound-term processing Lemmatization Lexical analysis Text chunking Stemming Sentence segmentation Word segmentation Automatic summarization Multi-document summarization Sentence extraction Text simplification Machine translation Computer-assisted Example-based Rule-based Statistical Transfer-based Neural Distributional semantics models BERT Document-term matrix Explicit semantic analysis fastText GloVe Language model large small Latent semantic analysis Long short-term memory Seq2seq Transformer Word embedding Word2vec Language resources, datasets and corpora Types and standards Corpus linguistics Lexical resource Linguistic Linked Open Data Machine-readable dictionary Parallel text PropBank Semantic network Simple Knowledge Organization System Speech corpus Text corpus Thesaurus (information retrieval) Treebank Universal Dependencies Data BabelNet Bank of English DBpedia FrameNet Google Ngram Viewer UBY WordNet Wikidata Automatic identification and data capture Speech recognition Speech segmentation Speech synthesis Natural language generation Topic model Document classification Dynamic topic model Latent Dirichlet allocation Pachinko allocation Computer-assisted reviewing Automated essay scoring Concordancer Grammar checker Predictive text Pronunciation assessment Spell checker Natural language user interface Chatbot Interactive fiction Prompt engineering Question answering Virtual assistant Voice user interface Visual-linguistic Automatic image annotation CLIP Multimodal sentiment analysis Optical character recognition Vision-language model Vision–language–action model Related Formal semantics Gensim Hallucination Natural Language Toolkit spaCy

---
Adapted from the Wikipedia article [Compound-term processing](https://en.wikipedia.org/wiki/Compound-term_processing) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Compound-term_processing?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.
