A Matrix-Based Heuristic Algorithm for Extracting Multiword Expressions from a Corpus
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AZUKZCEJQ" target="_blank" >RIV/00216208:11320/22:ZUKZCEJQ - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.mwe-1.7" target="_blank" >https://aclanthology.org/2022.mwe-1.7</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
A Matrix-Based Heuristic Algorithm for Extracting Multiword Expressions from a Corpus
Original language description
This paper describes an algorithm for automatically extracting multiword expressions (MWEs) from a corpus. The algorithm is node-based, i.e. extracts MWEs that contain the item specified by the user, using a fixed window-size around the node. The main idea is to detect the frequency anomalies that occur at the starting and ending points of an ngram that constitutes a MWE. This is achieved by locally comparing matrices of observed frequencies to matrices of expected frequencies, and determining, for each individual input, one or more sub-sequences that have the highest probability of being a MWE. Top-performing sub-sequences are then combined in a score-aggregation and ranking stage, thus producing a single list of score-ranked MWE candidates, without having to indiscriminately generate all possible sub-sequences of the input strings. The knowledge-poor and computationally efficient algorithm attempts to solve certain recurring problems in MWE extraction, such as the inability to deal with MWEs of arbitrary length, the repetitive counting of nested ngrams, and excessive sensitivity to frequency. Evaluation results show that the best-performing version generates top-50 precision values between 0.71 and 0.88 on Turkish and English data, and performs better than the baseline method even at n=1000.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 18th Workshop on Multiword Expressions @LREC2022
ISBN
979-10-95546-90-0
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
37-48
Publisher name
European Language Resources Association
Place of publication
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Event location
Marseille, France
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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