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Academic literature on the topic 'Multiwords'

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Published: 10 March 2023

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Journal articles on the topic "Multiwords"

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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.

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Bruyère, 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.

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Jost, 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.

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Piunno, 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.

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Abstract This paper focuses on a specific type of Multiword Expressions, particularly widespread in Italian as well as in other Romance languages: Multiword Modifiers, i.e. prepositional phrases functioning as modifiers of a noun (Multiword Adjectives) and of a verb (Multiword Adverbs). Exploiting both syntactic and semantic analysis, this paper explores the hypothesis that Multiword Modifiers are formed on the basis of regular syntactic templates, which can structure and organize the semantic information associated with words. In this perspective, after a brief presentation of Multiword Lexical Units and the class of Multiword Modifiers, the methodology and the general theoretical framework of this study will be explained. The last section is devoted to the analysis of some semantic relations frequently fulfilled by Multiword Modifiers of Italian, French and Spanish. This investigation aims at demonstrating that all Romance languages considered make a regular use of this kind of analytical resource in adjectival or adverbial function, showing similar patterns and syntactic templates.
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Boers, 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.

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In recent years, many educational linguists have emphasised the importance of drawing language learners’ attention to multiword units (i.e., strong collocations, idioms, etc.), because knowledge of such units is believed to help learners come across as fluent, native-like and accurate L2 speakers. We report a controlled experiment the results of which support this belief. The question now is how learners can be helped to commit multiword units to memory. We borrow insights from Cognitive Linguistics, which, contrary to other frameworks, holds that the meaning and the lexical composition of many multiword units is motivated rather than arbitrary. The article surveys experiments that were set up to measure the mnemonic effects of presenting multiword units (especially idioms) as semantically and/or phonologically motivated. The overall encouraging results are explainable by established theories of memory, such as ‘levels-of-processing’ and ‘dual coding’ models. At the same time, the results point to cognitive-style variables that may enhance or dampen the effectiveness of the proposed instructional methods.
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Villavicencio, 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.

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AbstractIn this paper, we provide an overview of research on multiword expressions (MWEs), from a natural language processing perspective. We examine methods developed for modelling MWEs that capture some of their linguistic properties, discussing their use for MWE discovery and for idiomaticity detection. We concentrate on their collocational and contextual preferences, along with their fixedness in terms of canonical forms and their lack of word-for-word translatatibility. We also discuss a sample of the MWE resources that have been used in intrinsic evaluation setups for these methods.
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Shin, 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.

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Arnon, 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.

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There is growing evidence that multiword information affects processing. In this paper, we look at the effect of word and multiword frequency on the phonetic duration of words in spontaneous speech to (a) extend previous findings and (b) ask whether the relation between word and multiword information changes across the frequency continuum. If highly frequent sequences are stored holistically, then the effect of word frequency should disappear. If alternatively, increased sequence usage causes a change in the prominence of word and multiword information, we should see reduced effects of word frequency, and increased effects of sequence frequency for high frequency sequences. We first extend previous findings by showing that trigram frequency affects single word duration, even when controlling for word predictability. We then show that the effect of trigram frequency increases while the effect of word frequency decreases — but does not disappear — for highly frequent sequences. The findings provide further support for the effect of multiword information on processing and document the growing prominence of multiword information with repeated usage.
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Green, 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.

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Multiword expressions lie at the syntax/semantics interface and have motivated alternative theories of syntax like Construction Grammar. Until now, however, syntactic analysis and multiword expression identification have been modeled separately in natural language processing. We develop two structured prediction models for joint parsing and multiword expression identification. The first is based on context-free grammars and the second uses tree substitution grammars, a formalism that can store larger syntactic fragments. Our experiments show that both models can identify multiword expressions with much higher accuracy than a state-of-the-art system based on word co-occurrence statistics. We experiment with Arabic and French, which both have pervasive multiword expressions. Relative to English, they also have richer morphology, which induces lexical sparsity in finite corpora. To combat this sparsity, we develop a simple factored lexical representation for the context-free parsing model. Morphological analyses are automatically transformed into rich feature tags that are scored jointly with lexical items. This technique, which we call a factored lexicon, improves both standard parsing and multiword expression identification accuracy.
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Yan, 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.

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More and more attention has been paid to the processing of multiword expressions in recent years. This paper reviews empirical studies that have examined the effects of frequency and congruency and their interactive role on the processing of multiword expressions. The results indicated that although frequency and congruency influence the processing of all kinds of multiword expressions, the studies mostly concentrate on collocations; their interactive role with proficiency has not been specified; research exploring the effect of congruency is limited to translational congruency. Future studies can compare the difference in the processing of different kinds of multiword expressions.
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Dissertations / Theses on the topic "Multiwords"

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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.

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Waszczuk, Jakub. "Leveraging MWEs in practical TAG parsing : towards the best of the two worlds." Thesis, Tours, 2017. http://www.theses.fr/2017TOUR4024/document.

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Dans ce mémoire, nous nous penchons sur les expressions polylexicales (EP) et leurs relations avec l’analyse syntaxique, la tâche qui consiste à déterminer les relations syntaxiques entre les mots dans une phrase donnée. Le défi que posent les EP dans ce contexte, par rapport aux expressions linguistiques régulières, provient de leurs propriétés parfois inattendues qui les rendent difficiles à gérer dans te traitement automatique des langues. Dans nos travaux, nous montrons qu’il est pourtant possible de profiter de ce cette caractéristique des EP afin d’améliorer les résultats d’analyse syntaxique. Notamment, avec les grammaires d’arbres adjoints (TAGs), qui fournissent un cadre naturel et puissant pour la modélisation des EP, ainsi qu’avec des stratégies de recherche basées sur l’algorithme A* , il est possible d’obtenir des gains importants au niveau de la vitesse sans pour autant détériorer la qualité de l’analyse syntaxique. Cela contraste avec des méthodes purement statistiques qui, malgré l’efficacité, ne fournissent pas de solutions satisfaisantes en ce qui concerne les EP. Nous proposons un analyseur syntaxique novateur qui combine les grammaires TAG avec La technique A*, axé sur la prédiction des EP, dont les fonctionnalités permettent des applications à grande échelle, facilement extensible au contexte probabiliste
In 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
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Su, Kim Nam. "Statistical modeling of multiword expressions." Connect to thesis, 2008. http://repository.unimelb.edu.au/10187/3147.

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In natural languages, words can occur in single units called simplex words or in a group of simplex words that function as a single unit, called multiword expressions (MWEs). Although MWEs are similar to simplex words in their syntax and semantics, they pose their own sets of challenges (Sag et al. 2002). MWEs are arguably one of the biggest roadblocks in computational linguistics due to the bewildering range of syntactic, semantic, pragmatic and statistical idiomaticity they are associated with, and their high productivity. In addition, the large numbers in which they occur demand specialized handling. Moreover, dealing with MWEs has a broad range of applications, from syntactic disambiguation to semantic analysis in natural language processing (NLP) (Wacholder and Song 2003; Piao et al. 2003; Baldwin et al. 2004; Venkatapathy and Joshi 2006).
Our 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).
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Korkontzelos, Ioannis. "Unsupervised learning of multiword expressions." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/2091/.

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Multiword expressions are expressions consisting of two or more words that correspond to some conventional way of saying things (Manning & Schutze 1999). Due to the idiomatic nature of many of them and their high frequency of occurence in all sorts of text, they cause problems in many Natural Language Processing (NLP) applications and are frequently responsible for their shortcomings. Efficiently recognising multiword expressions and deciding the degree of their idiomaticity would be useful to all applications that require some degree of semantic processing, such as question-answering, summarisation, parsing, language modelling and language generation. In this thesis we investigate the issues of recognising multiword expressions, domainspecific or not, and of deciding whether they are idiomatic. Moreover, we inspect the extent to which multiword expressions can contribute to a basic NLP task such as shallow parsing and ways that the basic property of multiword expressions, idiomaticity, can be employed to define a novel task for Compositional Distributional Semantics (CDS). The results show that it is possible to recognise multiword expressions and decide their compositionality in an unsupervised manner, based on cooccurrence statistics and distributional semantics. Further, multiword expressions are beneficial for other fundamental applications of Natural Language Processing either by direct integration or as an evaluation tool. In particular, termhood-based methods, which are based on nestedness information, are shown to outperform unithood-based methods, which measure the strength of association among the constituents of a multi-word candidate term. A simple heuristic was proved to perform better than more sophisticated methods. A new graph-based algorithm employing sense induction is proposed to address multiword expression compositionality and is shown to perform better than a standard vector space model. Its parameters were estimated by an unsupervised scheme based on graph connectivity. Multiword expressions are shown to contribute to shallow parsing. Moreover, they are used to define a new evaluation task for distributional semantic composition models.
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Taslimipoor, Shiva. "Automatic identification and translation of multiword expressions." Thesis, University of Wolverhampton, 2018. http://hdl.handle.net/2436/622068.

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Multiword Expressions (MWEs) belong to a class of phraseological phenomena that is ubiquitous in the study of language. They are heterogeneous lexical items consisting of more than one word and feature lexical, syntactic, semantic and pragmatic idiosyncrasies. Scholarly research on MWEs benefits both natural language processing (NLP) applications and end users. This thesis involves designing new methodologies to identify and translate MWEs. In order to deal with MWE identification, we first develop datasets of annotated verb-noun MWEs in context. We then propose a method which employs word embeddings to disambiguate between literal and idiomatic usages of the verb-noun expressions. Existence of expression types with various idiomatic and literal distributions leads us to re-examine their modelling and evaluation. We propose a type-aware train and test splitting approach to prevent models from overfitting and avoid misleading evaluation results. Identification of MWEs in context can be modelled with sequence tagging methodologies. To this end, we devise a new neural network architecture, which is a combination of convolutional neural networks and long-short term memories with an optional conditional random field layer on top. We conduct extensive evaluations on several languages demonstrating a better performance compared to the state-of-the-art systems. Experiments show that the generalisation power of the model in predicting unseen MWEs is significantly better than previous systems. In order to find translations for verb-noun MWEs, we propose a bilingual distributional similarity approach derived from a word embedding model that supports arbitrary contexts. The technique is devised to extract translation equivalents from comparable corpora which are an alternative resource to costly parallel corpora. We finally conduct a series of experiments to investigate the effects of size and quality of comparable corpora on automatic extraction of translation equivalents.
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Cordeiro, Silvio Ricardo. "Distributional models of multiword expression compositionality prediction." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0501/document.

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Les systèmes de traitement automatique des langues reposent souvent sur l'idée que le langage est compositionnel, c'est-à-dire que le sens d'une entité linguistique peut être déduite à partir du sens de ses parties. Cette supposition ne s’avère pas vraie dans le cas des expressions polylexicales (EPLs). Par exemple, une "poule mouillée" n'est ni une poule, ni nécessairement mouillée. Les techniques pour déduire le sens des mots en fonction de leur distribution dans le texte ont obtenu de bons résultats sur plusieurs tâches, en particulier depuis l'apparition des word embeddings. Cependant, la représentation des EPLs reste toujours un problème non résolu. En particulier, on ne sait pas comment prédire avec précision, à partir des corpus, si une EPL donnée doit être traitée comme une unité indivisible (p.ex. "carton plein") ou comme une combinaison du sens de ses parties (p.ex. "eau potable"). Cette thèse propose un cadre méthodologique pour la prédiction de compositionnalité d'EPLs fondé sur des représentations de la sémantique distributionnelle, que nous instancions à partir d’une variété de paramètres. Nous présenterons une évaluation complète de l'impact de ces paramètres sur trois nouveaux ensembles de données modélisant la compositionnalité d'EPLs, en anglais, français et portugais. Finalement, nous présenterons une évaluation extrinsèque des niveaux de compositionnalité prédits par le modèle dans le contexte d’un système d'identification d'EPLs. Les résultats suggèrent que le choix spécifique de modèle distributionnel et de paramètres de corpus peut produire des prédictions de compositionnalité qui sont comparables à celles présentées dans l'état de l'art
Natural 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
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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.

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Sistemas de processamento de linguagem natural baseiam-se com frequência na hipótese de que a linguagem humana é composicional, ou seja, que o significado de uma entidade linguística pode ser inferido a partir do significado de suas partes. Essa expectativa falha no caso de expressões multipalavras (EMPs). Por exemplo, uma pessoa caracterizada como pão-duro não é literalmente um pão, e também não tem uma consistência molecular mais dura que a de outras pessoas. Técnicas computacionais modernas para inferir o significado das palavras com base na sua distribuição no texto vêm obtendo um considerável sucesso em múltiplas tarefas, especialmente após o surgimento de abordagens de word embeddings. No entanto, a representação de EMPs continua a ser um problema em aberto na área. Em particular, não existe um método consolidado que prediga, com base em corpora, se uma determinada EMP deveria ser tratada como unidade indivisível (por exemplo olho gordo) ou como alguma combinação do significado de suas partes (por exemplo tartaruga marinha). Esta tese propõe um modelo de predição de composicionalidade de EMPs com base em representações de semântica distribucional, que são instanciadas no contexto de uma variedade de parâmetros. Também é apresentada uma avaliação minuciosa do impacto desses parâmetros em três novos conjuntos de dados que modelam a composicionalidade de EMP, abrangendo EMPs em inglês, francês e português. Por fim, é apresentada uma avaliação extrínseca dos níveis previstos de composicionalidade de EMPs, através da tarefa de identificação de EMPs. Os resultados obtidos sugerem que a escolha adequada do modelo distribucional e de parâmetros de corpus pode produzir predições de composicionalidade que são comparáveis às observadas no estado da arte.
Natural 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.
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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/.

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The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations. This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions. This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena.
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Obermeier, 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.

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Language Arts
Ed.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
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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.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Muitos 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.
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Books on the topic "Multiwords"

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Ramisch, Carlos. Multiword Expressions Acquisition. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09207-2.

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Wang, Shan. Chinese Multiword Expressions. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-8510-0.

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Brunner, Annelen. Wortverbindungsfelder - fields of multiword expressions. Mannheim: Institut für Deutsche Sprache, Bibliothek, 2015.

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Mitkov, 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.

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Life, James. Patterns in English Multiword Vocabulary. Createspace Independent Publishing Platform, 2016.

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Bielinskienė, 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.

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Bielinskienė, 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.

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Wang, Shan. Chinese Multiword Expressions: Theoretical and Practical Perspectives. Springer, 2019.

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Wang, Shan. Chinese Multiword Expressions: Theoretical and Practical Perspectives. Springer, 2019.

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Wang, Shan. Chinese Multiword Expressions: Theoretical and Practical Perspectives. Springer Singapore Pte. Limited, 2021.

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Book chapters on the topic "Multiwords"

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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.

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Thanopoulos, 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.

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Zwier, 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.

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Ramisch, 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.

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Ramisch, 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.

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Ramisch, 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.

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Ramisch, 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.

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Ramisch, 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.

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Ramisch, 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.

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Ramisch, 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.

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Conference papers on the topic "Multiwords"

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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.

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Zakharov, 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.

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This paper is part of a larger study that aims to create the first quantitative grammar of the Russian prepositional system. The present study deals with Russian secondary multiword prepositions. Prepositions are a heterogeneous class consisting of a small group of about 25 primary prepositions and hundreds of secondary ones, the latter being motivated by content words (nouns, adverbs, verbs), which may be combined with primary prepositions to form multiword prepositions (MWPs). A strict division between secondary multiword prepositions and equivalent free word combinations is not specified. This is a task for a special corpus-based research. Prepositions are characterized as function words used to express various relationships between main and dependent members of a phrase. The difficulty is that relations expressed by prepositions are multi-sided, grammatical and lexical. Primary prepositions are said to have no real lexical meaning. It is not quite true as regards primary prepositions and even more so for secondary ones. Prepositions express semantic relations between words, and their meanings directly correspond to these relations. Multiword prepositions perform the grammatical function of a preposition in a certain position of a syntactic structure in some contexts and can be a free combination in others. This paper is devoted to the statistical analysis of the use of multiword prepositions in corpora. The features of multiword prepositions in the function of a preposition are described. Statistical data on the ratio of the use of individual multiword expressions as prepositional units and as free combinations are provided
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Dias, 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.

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Baldwin, Timothy. "Compositionality and multiword expressions." In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1613692.1613693.

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Goodkind, 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.

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Tan, 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.

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Light verb constructions (LVC) are an interesting phenomenon that occurs in many languages. It is a category of verbal Multiword Expressions (MWE) and has the canonical form of verb+noun (Constant et al., 2017; Cordeiro and Candito, 2019; Nagy T., Rácz and Vincze, 2020). Examples of LVCs include give help, make a decision, and take a walk. Identifying LVCs is essential for many natural language processing (NLP) applications which include summarization, machine translation, semantic parsing, question answering, and information extraction. The importance of LVC identification to these downstream applications has recently spurred a growing volume of work in both the field of linguistics as well as computational linguistics in various languages as it can potentially increase the performance of these tasks. This paper presents a review of existing work related to LVC identification by summarizing gaps identified and proposing some future work that could bring novel contributions. Keywords: Light verb constructions, Multiword expressions, Natural Language Processing
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Berk, 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.

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Potemkin, 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.

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Villavicencio, 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.

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Papka, 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.

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