--- title: "Automatic Identification of Non-compositional — Lin 1999" description: "The paper tackles the automatic detection of non-compositional phrases (idioms such as \"red tape\" or \"spill gut\")." authors: ["Dekang Lin"] year: 1999 venue: "Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics" source: "https://aclanthology.org/P99-1041/" doi: "10.3115/1034678.1034730" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/lin-1999-automatic-identification-non-compositional-phrases/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/lin-1999-automatic-identification-non-compositional-phrases/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [1999](https://md.research.elamur.ai/free/papers/1999/) / Automatic Identification of Non-compositional Phrases # Automatic Identification of Non-compositional Phrases **Authors:** [Dekang Lin](https://md.research.elamur.ai/free/authors/dekang-lin/) · **Year:** 1999 · **Venue:** Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/P99-1041/) · [PDF](https://aclanthology.org/P99-1041.pdf) · [DOI 10.3115/1034678.1034730](https://doi.org/10.3115/1034678.1034730) · [OpenAlex](https://openalex.org/W2439228446) **Citations:** 207 citations (openalex-snapshot · 2026-10-14) · [81](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper tackles the automatic detection of non-compositional phrases (idioms such as "red tape" or "spill gut"). Their meaning cannot be built from their parts, which can mislead machine translation, query expansion in information retrieval, and similarity-based word sense disambiguation. Lin's core idea is that an idiomatic phrase behaves differently in distribution from phrases made by swapping one of its words for a near-synonym. A large newspaper corpus was parsed with Minipar. Dependency triples were filtered into a collocation database, and a corpus-based thesaurus supplied similar words for each head and modifier. For every collocation, mutual information is computed along with a confidence interval derived from a normal approximation to the binomial frequency count. A phrase is labelled non-compositional when no substituted variant exists whose 95% interval overlaps its own. Comparison with WordNet was not feasible because its multiword entries are already single lexical items in the parser. The output was therefore judged by manually inspecting results for a sample of words. The approach matters because it supplies idiom lists without hand-built resources. ## Key points - Input data: a 125-million-word newspaper corpus parsed with Minipar (a descendant of Principar). This yielded roughly 80 million dependency relationships, which were reduced to about 11 million unique collocations using a log-likelihood ratio threshold of 0.5. - Thesaurus: built with Lin's (1998) similarity measure, it covers 11,839 nouns, 3,639 verbs and 5,658 adjectives/adverbs that occur at least 100 times. - Illustrative cases: - "spill gut" (13 occurrences, MI 6.24) has no attested substitutes. - "red tape" (259 occurrences, MI 5.87) far exceeds "yellow tape" (MI 3.75). - Compositional "economic impact" has many variants with comparable MI values. - Significance test: confidence intervals use z = 1.96 for the 95% level. For example, "make difference" (MI 2.928) and "make change" (MI 2.194) have disjoint intervals, so their MI values count as significantly different. - Evaluation sample: - The five most frequent open-class words: have, company, make, do, take. - Five words with frequency ranks 2000–2004, such as path, lock, resort and column. - An appendix also lists results obtained without a parser, using word proximity. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) Topics: [Word sense disambiguation](https://md.research.elamur.ai/free/topics/word-sense-disambiguation/) ## Cited by Cited by 81 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 68 not yet. - [An Unsupervised Approach to Prepositional Phrase Attachment using Contextually Similar Words](https://md.research.elamur.ai/free/papers/aclbulk-2000-pantel-unsupervised-approach-prepositional-phrase-attachment/) - [Differentiating Types of Verb Particle Constructions](https://md.research.elamur.ai/free/papers/aclbulk-2004-patrick-differentiating-types-verb-particle-constructions/) - [Classification of Verb-Particle Constructions with the Google Web1T Corpus](https://md.research.elamur.ai/free/papers/aclbulk-2008-kummerfeld-classification-verb-particle-constructions-google/) - [An Empirical Model of Multiword Expression Decomposability](https://md.research.elamur.ai/free/papers/baldwin-2003-empirical-model-multiword-expression-decomposability/) - [Pulling their Weight: Exploiting Syntactic Forms for the Automatic Identification of Idiomatic Expressions in Context](https://md.research.elamur.ai/free/papers/cook-2007-pulling-their-weight-exploiting-syntactic/) - [Automatically Constructing a Lexicon of Verb Phrase Idiomatic Combinations](https://md.research.elamur.ai/free/papers/fazly-2006-automatically-constructing-lexicon-verb-phrase/) - [Unsupervised Type and Token Identification of Idiomatic Expressions](https://md.research.elamur.ai/free/papers/fazly-2009-unsupervised-type-token-identification-idiomatic/) - [Automatic Identification of Non-Compositional Multi-Word Expressions using Latent Semantic Analysis](https://md.research.elamur.ai/free/papers/katz-2006-automatic-identification-non-compositional-multi/) - [AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://md.research.elamur.ai/free/papers/madabushi-2021-astitchinlanguagemodels-dataset-methods-exploration-idiomaticity/) - [Dependency-Based Construction of Semantic Space Models](https://md.research.elamur.ai/free/papers/pado-2007-dependency-based-construction-semantic-space/) - [An Empirical Study on Compositionality in Compound Nouns](https://md.research.elamur.ai/free/papers/reddy-2011-empirical-study-compositionality-compound-nouns/) - [The semantic transparency of English compound nouns](https://md.research.elamur.ai/free/papers/schafer-2018-semantic-transparency-english-compound/) - [Unsupervised Recognition of Literal and Non-Literal Use of Idiomatic Expressions](https://md.research.elamur.ai/free/papers/sporleder-2009-unsupervised-recognition-literal-non-literal/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{lin1999automatic, title = {{Automatic Identification of Non-compositional Phrases}}, author = {Lin, Dekang}, booktitle = {Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics}, year = {1999}, doi = {10.3115/1034678.1034730}, url = {https://doi.org/10.3115/1034678.1034730} } ``` - [← previous Authoritative Sources in a Hyperlinked Environment](https://md.research.elamur.ai/free/papers/kleinberg-1999-authoritative-sources-hyperlinked-environment/) - [next → Confused Language as a Deliberate Literary Device in Biblical Hebrew Narrative](https://md.research.elamur.ai/free/papers/hbo-1999-rendsburg-confused-language-deliberate-literary-device/) - [↑ back to papers of 1999](https://md.research.elamur.ai/free/papers/1999/) ## References 5 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Y. Choueka. 1988. Looking for needles in a haystack or locating interesting collocational expressions in large textual databases. In Proceedings of the RIA O Conference on User-Oriented Content-Based Text and Image Handling, Cambridge, MA, March 21-24. [find](https://scholar.google.com/scholar?q=Y%2E+Choueka%2E+1988%2E+Looking+for+needles+in+a+haystack+or+locating+interesting+collocational+expressions+in+large+textual+databases%2E+In+Proceedings+of+the+RIA+O+Conference+on+User%2DOriented+Content%2DBased+) 2. Ido Dagan and Alon Itai. 1994. Word sense disambiguation using a second language monolingual corpus. Computational Linguistics, 20(4):563-596. [find](https://scholar.google.com/scholar?q=Ido+Dagan+and+Alon+Itai%2E+1994%2E+Word+sense+disambiguation+using+a+second+language+monolingual+corpus%2E+Computational+Linguistics%2C+20%284%29%3A563%2D596%2E) 3. Ted Dunning. 1993. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61-74, March. [find](https://scholar.google.com/scholar?q=Ted+Dunning%2E+1993%2E+Accurate+methods+for+the+statistics+of+surprise+and+coincidence%2E+Computational+Linguistics%2C+19%281%29%3A61%2D74%2C+March%2E) [our card](https://md.research.elamur.ai/free/papers/dunning-1993-accurate-methods-statistics-surprise-coincidence/) 4. Marti A. Hearst. 1998. Automated discovery of wordnet relations. In C. Fellbaum, editor, WordNet: An Electronic Lezical Database, pages 131-151. MIT Press. [find](https://scholar.google.com/scholar?q=Marti+A%2E+Hearst%2E+1998%2E+Automated+discovery+of+wordnet+relations%2E+In+C%2E+Fellbaum%2C+editor%2C+WordNet%3A+An+Electronic+Lezical+Database%2C+pages+131%2D151%2E+MIT+Press%2E) 5. Table 5: Comparison with (Tapanainen et al., 1998) verb-object take toll mi + ntc similar collocation [find](https://scholar.google.com/scholar?q=Table+5%3A+Comparison+with+%28Tapanainen+et+al%2E%2C+1998%29+verb%2Dobject+take+toll+mi+%2B+ntc+similar+collocation)