A Matrix-Based Heuristic Algorithm for Extracting Multiword Expressions from a Corpus

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

A Matrix-Based Heuristic Algorithm for Extracting Multiword Expressions from a Corpus

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Matrix-Based Heuristic Algorithm for Extracting Multiword Expressions from a Corpus

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

12

Strana od-do

37-48

Název nakladatele

European Language Resources Association

Místo vydání

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Místo konání akce

Marseille, France

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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