Academic literature on the topic 'Multiwords'
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Journal articles on the topic "Multiwords"
COWIE, A. P. "Multiwords Units in Newspaper Language." Cahiers de l'Institut de Linguistique de Louvain 17, no. 1 (January 1, 1991): 101–16. http://dx.doi.org/10.2143/cill.17.1.2016699.
Full textBruyère, Véronique, Olivier Carton, Alexandre Decan, Olivier Gauwin, and Jef Wijsen. "An aperiodicity problem for multiwords." RAIRO - Theoretical Informatics and Applications 46, no. 1 (November 23, 2011): 33–50. http://dx.doi.org/10.1051/ita/2011131.
Full textJost, David, and Win Carus. "Computing Business Multiwords: Computational Linguistics in Support of Lexicography." Dictionaries: Journal of the Dictionary Society of North America 24, no. 1 (2003): 59–83. http://dx.doi.org/10.1353/dic.2003.0001.
Full textPiunno, Valentina. "Multiword Modifiers in some Romance languages. Semantic formats and syntactic templates." Yearbook of Phraseology 7, no. 1 (October 1, 2016): 3–34. http://dx.doi.org/10.1515/phras-2016-0002.
Full textBoers, Frank, June Eyckmans, and Hélène Stengers. "Motivating multiword units." EUROSLA Yearbook 6 (July 20, 2006): 169–90. http://dx.doi.org/10.1075/eurosla.6.11boe.
Full textVillavicencio, Aline, and Marco Idiart. "Discovering multiword expressions." Natural Language Engineering 25, no. 06 (September 11, 2019): 715–33. http://dx.doi.org/10.1017/s1351324919000494.
Full textShin, Dongkwang, and Yuah V. Chon. "A Multiword Unit Analysis : COCA Multiword Unit List 20 and ColloGram." Journal of AsiaTEFL 16, no. 2 (June 30, 2019): 608–23. http://dx.doi.org/10.18823/asiatefl.2019.16.2.11.608.
Full textArnon, Inbal, and Uriel Cohen Priva. "Time and again." Mental Lexicon 9, no. 3 (December 31, 2014): 377–400. http://dx.doi.org/10.1075/ml.9.3.01arn.
Full textGreen, Spence, Marie-Catherine de Marneffe, and Christopher D. Manning. "Parsing Models for Identifying Multiword Expressions." Computational Linguistics 39, no. 1 (March 2013): 195–227. http://dx.doi.org/10.1162/coli_a_00139.
Full textYan, Feifei. "A Review of the Effects of Frequency and Congruency on the Processing of Multiword Expressions." International Journal of Linguistics, Literature and Translation 5, no. 5 (May 18, 2022): 165–73. http://dx.doi.org/10.32996/ijllt.2022.5.5.20.
Full textDissertations / Theses on the topic "Multiwords"
Monti, Johanna. "Multi-word unit processing in machine translation. Developing and using language resources for multi-word unit processing in machine translation." Doctoral thesis, Universita degli studi di Salerno, 2015. http://hdl.handle.net/10556/2042.
Full textWaszczuk, Jakub. "Leveraging MWEs in practical TAG parsing : towards the best of the two worlds." Thesis, Tours, 2017. http://www.theses.fr/2017TOUR4024/document.
Full textIn this thesis, we focus on multiword expressions (MWEs) and their relationships with syntactic parsing. The latter task consists in retrieving the syntactic relations holding between the words in a given sentence. The challenge of MWEs in this respect is that, in contrast to regular linguistic expressions, they exhibit various irregular properties which make them harder to deal with in natural language processing. In our work, we show that the challenge of the MWE-related irregularities can be turned into an advantage in practical symbolic parsing. Namely, with tree adjoining grammars (TAGs), which provide first-cLass support for MWEs, and A* search strategies, considerable speed-up gains can be achieved by promoting MWE-based analyses with virtually no loss in syntactic parsing accuracy. This is in contrast to purely statistical state-of-the-art parsers, which, despite efficiency, provide no satisfactory support for MWEs. We contribute a TAG-A* -MWE-aware parsing architecture with facilities (grammar compression and feature structures) enabling real-world applications, easily extensible to a probabilistic framework
Su, Kim Nam. "Statistical modeling of multiword expressions." Connect to thesis, 2008. http://repository.unimelb.edu.au/10187/3147.
Full textOur goals in this research are: to use computational techniques to shed light on the underlying linguistic processes giving rise to MWEs across constructions and languages; to generalize existing techniques by abstracting away from individual MWE types; and finally to exemplify the utility of MWE interpretation within general NLP tasks.
In this thesis, we target English MWEs due to resource availability. In particular, we focus on noun compounds (NCs) and verb-particle constructions (VPCs) due to their high productivity and frequency.
Challenges in processing noun compounds are: (1) interpreting the semantic relation (SR) that represents the underlying connection between the head noun and modifier(s); (2) resolving syntactic ambiguity in NCs comprising three or more terms; and (3) analyzing the impact of word sense on noun compound interpretation. Our basic approach to interpreting NCs relies on the semantic similarity of the NC components using firstly a nearest-neighbor method (Chapter 5), then verb semantics based on the observation that it is often an underlying verb that relates the nouns in NCs (Chapter 6), and finally semantic variation within NC sense collocations, in combination with bootstrapping (Chapter 7).
Challenges in dealing with verb-particle constructions are: (1) identifying VPCs in raw text data (Chapter 8); and (2) modeling the semantic compositionality of VPCs (Chapter 5). We place particular focus on identifying VPCs in context, and measuring the compositionality of unseen VPCs in order to predict their meaning. Our primary approach to the identification task is to adapt localized context information derived from linguistic features of VPCs to distinguish between VPCs and simple verb-PP combinations. To measure the compositionality of VPCs, we use semantic similarity among VPCs by testing the semantic contribution of each component.
Finally, we conclude the thesis with a chapter-by-chapter summary and outline of the findings of our work, suggestions of potential NLP applications, and a presentation of further research directions (Chapter 9).
Korkontzelos, Ioannis. "Unsupervised learning of multiword expressions." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/2091/.
Full textTaslimipoor, Shiva. "Automatic identification and translation of multiword expressions." Thesis, University of Wolverhampton, 2018. http://hdl.handle.net/2436/622068.
Full textCordeiro, Silvio Ricardo. "Distributional models of multiword expression compositionality prediction." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0501/document.
Full textNatural language processing systems often rely on the idea that language is compositional, that is, the meaning of a linguistic entity can be inferred from the meaning of its parts. This expectation fails in the case of multiword expressions (MWEs). For example, a person who is a "sitting duck" is neither a duck nor necessarily sitting. Modern computational techniques for inferring word meaning based on the distribution of words in the text have been quite successful at multiple tasks, especially since the rise of word embedding approaches. However, the representation of MWEs still remains an open problem in the field. In particular, it is unclear how one could predict from corpora whether a given MWE should be treated as an indivisible unit (e.g. "nut case") or as some combination of the meaning of its parts (e.g. "engine room"). This thesis proposes a framework of MWE compositionality prediction based on representations of distributional semantics, which we instantiate under a variety of parameters. We present a thorough evaluation of the impact of these parameters on three new datasets of MWE compositionality, encompassing English, French and Portuguese MWEs. Finally, we present an extrinsic evaluation of the predicted levels of MWE compositionality on the task of MWE identification. Our results suggest that the proper choice of distributional model and corpus parameters can produce compositionality predictions that are comparable to the state of the art
Cordeiro, Silvio Ricardo. "Distributional models of multiword expression compositionality prediction." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174519.
Full textNatural language processing systems often rely on the idea that language is compositional, that is, the meaning of a linguistic entity can be inferred from the meaning of its parts. This expectation fails in the case of multiword expressions (MWEs). For example, a person who is a sitting duck is neither a duck nor necessarily sitting. Modern computational techniques for inferring word meaning based on the distribution of words in the text have been quite successful at multiple tasks, especially since the rise of word embedding approaches. However, the representation of MWEs still remains an open problem in the field. In particular, it is unclear how one could predict from corpora whether a given MWE should be treated as an indivisible unit (e.g. nut case) or as some combination of the meaning of its parts (e.g. engine room). This thesis proposes a framework of MWE compositionality prediction based on representations of distributional semantics, which we instantiate under a variety of parameters. We present a thorough evaluation of the impact of these parameters on three new datasets of MWE compositionality, encompassing English, French and Portuguese MWEs. Finally, we present an extrinsic evaluation of the predicted levels of MWE compositionality on the task of MWE identification. Our results suggest that the proper choice of distributional model and corpus parameters can produce compositionality predictions that are comparable to the state of the art.
Alghamdi, Ayman Ahmad O. "A computational lexicon and representational model for Arabic multiword expressions." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22821/.
Full textObermeier, Andrew Stanton. "Multiword Units at the Interface: Deliberate Learning and Implicit Knowledge Gains." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/360635.
Full textEd.D.
Multiword units (MWUs) is a term used in the current study to broadly cover what second language acquisition (SLA) researchers refer to as collocations, conventional expressions, chunks, idioms, formulaic sequences, or other such terms, depending on their research perspective. They are ubiquitous in language and essential in both first language (L1) and second language (L2) acquisition. Although MWUs are typically learned implicitly while using language naturally in both of these types of acquisition, the current study is an investigation of whether they are acquired in implicit knowledge when they are learned explicitly in a process called deliberate paired association learning. In SLA research, it is widely accepted that explicit knowledge is developed consciously and implicit knowledge is developed subconsciously. It is also believed that there is little crossover from explicit learning to implicit knowledge. However, recent research has cast doubt on this assumption. In a series of priming experiments, Elgort (2007, 2011) demonstrated that the formal and semantic lexical representations of deliberately learned pseudowords were accessed fluently and integrated into the mental lexicon, convincing evidence that deliberately learned words are immediately acquired in implicit knowledge. The current study aimed to extend these findings to MWUs in a psycholinguistic experiment that tested for implicit knowledge gains resulting from deliberate learning. Participants’ response times (RTs) were measured in three ways, on two testing instruments. First, subconscious formal recognition processing was measured in a masked repetition priming lexical decision task. In the second instrument, a self-paced reading task, both formulaic sequencing and semantic association gains were measured. The experiment was a counterbalanced, within-subjects design; so all comparisons were between conditions on items. Results were analyzed in a repeated measures linear mixed-effects model with participants and items as crossed random effects. The dependent variable was RTs on target words. The primary independent variable was learning condition: half of the critical MWUs were learned and half of them were not. The secondary independent variable was MWU composition at two levels: literal and figurative. The masked priming lexical decision task results showed that priming effects increased especially for learned figurative MWUs, evidence that implicit knowledge gains were made on their formal and semantic lexical representations as a result of deliberate learning. Results of the self-paced reading task were analyzed from two perspectives, but were less conclusive with regard to the effects of deliberate learning. Regarding formulaic sequencing gains, literal MWUs showed the most evidence of acquisition, but this happened as a result of both incidental and deliberate learning. With regard to semantic associations, it was shown that deliberate learning had similar effects on both literal and figurative MWUs. However, a serendipitous finding from this aspect of the self-paced reading results showed clearly that literal MWUs reliably primed semantic associations and sentence processing more strongly than figurative MWUs did, both before and after deliberate learning. In sum, results revealed that the difficulties learners have with developing fluent processing of figurative MWUs can be lessened by deliberate learning. On the other hand, for literal MWUs incidental learning is adequate for incrementally developing representation strength.
Temple University--Theses
GARRAO, MILENA DE UZEDA. "THE CORPUS NEVER LIES: ON THE IDENTIFICATION AND USE OF MULTIWORD EXPRESSIONS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8873@1.
Full textMuitos estudos recentes sobre a identificação e uso de combinações multivocabulares (CMs) adotam uma perspectiva representacionista do significado da palavra. Este estudo propõe que é muito mais interessante identificar as CMs por um olhar não-representacionista. A metodologia proposta foi testada em CMs do tipo V+SN, um padrão bastante freqüente no português do Brasil (PB). Trata-se de uma análise estatística com base em córpus que pode ser resumida em três etapas: 1) córpus robusto do PB como base de análise, 2) aplicação de um teste estatístico ao córpus, a saber, teste de Logaritmo de Verossimilhança (Banerjee e Pedersen, 2003), para detecção das CMs mais freqüentes com padrão V+SN (como tomar café) e exclusão de co-ocorrências sintáticas aleatórias dos mesmos itens lexicais, 3) aplicação de Medidas de Similaridade (Baeza-Yates e Ribeiro-Neto, 1999) entre todos os parágrafos contendo uma certa CM (por exemplo, fazer campanha) e todos os parágrafos contendo o substantivo fora da CM (campanha). Esta última etapa foi utilizada para avaliar o grau de composicionalidade da CM. Pôde-se concluir que quanto maior a similaridade entre os parágrafos contendo a CM e os parágrafos contendo o substantivo fora da expressão, maior será o grau de composicionalidade da CM. Por essa razão, este estudo tem um impacto tanto teórico quanto prático para a semântica.
A considerable amount of recent researches on defining multi-word expressions´ (MWE) phenomenon has an underlying representational framework of word meaning. In this study we claim that it is much more interesting to view MWE from a non-representational perspective. By choosing this path, we avoid the time-consuming and controversial human intuitions to MWE identification and definition. Our methodology was tested on Brazilian Portuguese verbal phrases of V+NP pattern. It is a statistically-based corpus analysis which could be summed up as the following three sequent steps: 1) robust linguistic corpora as output, 2) application of a probabilistic test to the corpora, namely Log Likelihood test (Banerjee and Pedersen, 2003), in order to spot the Portuguese MWEs of V+NP pattern (such as tomar café) and disregard casual syntactic and not otherwise motivated co-occurrences of the same lexical items, 3) application of Similarity Measures (Baeza-Yates and Ribeiro-Neto, 1999) between all the paragraphs containing a certain MWE and all the paragraphs containing its separate noun. This latter step is crucial to assess the MWE compositionality level. We conclude that the higher are the similarity measures between the MWE (such as fazer campanha) and its separate noun (campanha), the more compositional will be the MWE. Therefore, we believe that this work has both a practical and a theoretical impact to semantics.
Books on the topic "Multiwords"
Ramisch, Carlos. Multiword Expressions Acquisition. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09207-2.
Full textWang, Shan. Chinese Multiword Expressions. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-8510-0.
Full textBrunner, Annelen. Wortverbindungsfelder - fields of multiword expressions. Mannheim: Institut für Deutsche Sprache, Bibliothek, 2015.
Find full textMitkov, Ruslan, Johanna Monti, Gloria Corpas Pastor, and Violeta Seretan, eds. Multiword Units in Machine Translation and Translation Technology. Amsterdam: John Benjamins Publishing Company, 2018. http://dx.doi.org/10.1075/cilt.341.
Full textLife, James. Patterns in English Multiword Vocabulary. Createspace Independent Publishing Platform, 2016.
Find full textBielinskienė, Agnė, Loic Boizou, Ieva Bumbulienė, Jolanta Kovalevskaitė, Tomas Krilavičius, Justina Mandravickaitė, Erika Rimkutė, and Laura Vilkaitė-Lozdienė. Database of Lithuanian multiword expressions. Baltic Institute of Advanced Technology, Vytautas Magnus University, 2019. http://dx.doi.org/10.7220/20.500.12259/240289.
Full textBielinskienė, Agnė, Loic Boizou, Ieva Bumbulienė, Jolanta Kovalevskaitė, Tomas Krilavičius, Justina Mandravickaitė, Erika Rimkutė, Jurgita Vaičenonienė, and Laura Vilkaitė-Lozdienė. The Database of Lithuanian multiword expressions. Vytautas Magnus University, 2022. http://dx.doi.org/10.7220/20.500.12259/240653.
Full textWang, Shan. Chinese Multiword Expressions: Theoretical and Practical Perspectives. Springer, 2019.
Find full textWang, Shan. Chinese Multiword Expressions: Theoretical and Practical Perspectives. Springer, 2019.
Find full textWang, Shan. Chinese Multiword Expressions: Theoretical and Practical Perspectives. Springer Singapore Pte. Limited, 2021.
Find full textBook chapters on the topic "Multiwords"
Arranz, Victoria, Jordi Atserias, and Mauro Castillo. "Multiwords and Word Sense Disambiguation." In Computational Linguistics and Intelligent Text Processing, 250–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_28.
Full textThanopoulos, Aristomenis, Nikos Fakotakis, and George Kokkinakis. "Identification of Multiwords as Preprocessing for Automatic Extraction of Lexical Similarities." In Text, Speech and Dialogue, 98–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39398-6_14.
Full textZwier, Lawrence J., and Frank Boers. "Multiword Expressions." In English L2 Vocabulary Learning and Teaching, 21–33. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003172994-3.
Full textRamisch, Carlos. "Introduction." In Multiword Expressions Acquisition, 1–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09207-2_1.
Full textRamisch, Carlos. "Definitions and Characteristics." In Multiword Expressions Acquisition, 23–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09207-2_2.
Full textRamisch, Carlos. "State of the Art in MWE Processing." In Multiword Expressions Acquisition, 53–102. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09207-2_3.
Full textRamisch, Carlos. "Evaluation of MWE Acquisition." In Multiword Expressions Acquisition, 105–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09207-2_4.
Full textRamisch, Carlos. "A New Framework for MWE Acquisition." In Multiword Expressions Acquisition, 127–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09207-2_5.
Full textRamisch, Carlos. "Application 1: Lexicography." In Multiword Expressions Acquisition, 159–79. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09207-2_6.
Full textRamisch, Carlos. "Application 2: Machine Translation." In Multiword Expressions Acquisition, 181–99. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09207-2_7.
Full textConference papers on the topic "Multiwords"
Silva, Edson Marchetti da, and Renato Rocha Souza. "Comparing three different techniques to retrieve documents using multiwords expressions." In 10th CONTECSI International Conference on Information Systems and Technology Management. Sao Paulo: TECSI, 2013. http://dx.doi.org/10.5748/9788599693094-10contecsi/ps-286.
Full textZakharov, Victor, Anastasia Golovina, and Irina Azarova. "STATISTICAL ANALYSIS OF RUSSIAN MULTIWORD PREPOSITIONS." In NORDSCI International Conference. SAIMA Consult Ltd, 2020. http://dx.doi.org/10.32008/nordsci2020/b1/v3/20.
Full textDias, Gaël. "Multiword unit hybrid extraction." In the ACL 2003 workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1119282.1119288.
Full textBaldwin, Timothy. "Compositionality and multiword expressions." In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1613692.1613693.
Full textGoodkind, Adam, and Andrew Rosenberg. "Muddying The Multiword Expression Waters: How Cognitive Demand Affects Multiword Expression Production." In Proceedings of the 11th Workshop on Multiword Expressions. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/w15-0914.
Full textTan, Kathleen Swee Neo, Tong Ming Lim, Chi Wee Tan, and Wei Wei Chew. "Review on Light Verb Constructions in Computational Linguistics." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1016.
Full textBerk, Gozde, Berna Erden, and Tunga Gungor. "Turkish verbal multiword expressions corpus." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404583.
Full textPotemkin, Serge. "Multiword Terms and Machine Translation." In Third International Conference, Europhras 2019, Computational and Corpus-Based Phraseology. Editions Tradulex, Geneva, 2019. http://dx.doi.org/10.26615/978-2-9701095-6-3_018.
Full textVillavicencio, Aline. "Multiword Expressions Under the Microscope." In Third International Conference, Europhras 2019, Computational and Corpus-Based Phraseology. Editions Tradulex, Geneva, 2019. http://dx.doi.org/10.26615/978-2-9701095-6-3_023.
Full textPapka, Ron, and James Allan. "Document classification using multiword features." In the seventh international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/288627.288648.
Full text