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ispartof: pages:23-32 ispartof: Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change pages:23-32 ispartof: 3rd Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics location:Dublin, Ireland date:26 May - 27 May 2022 status: published

ispartof: Nederlandse Taalkunde vol:25 issue:1 pages:101-123 status: published

Lectometric approaches measure distances between language varieties (dialects, sociolects, registers etc.) by aggregating over observed differences in the realizations of a set of linguistic variables. Inlexicallectometry, a variable consists of the alternative lexical expressions for one concept. Incorpus-basedlectometry, the observed realizations are culled from stratified corpora. Measuring semantically defined variables in corpora, and aggregating over them, poses specific methodological challenges that have been tackled in a number of studies (Heylen & Ruette 2013; Ruette et al. 2014; Ruette, Ehret & Szmrecsanyi 2016) with different statistical techniques, including Distributional Semantic Models. Yet so far, no general framework for corpus-based lexical lectometry has been formulated that systematically describes the issues and options in each step of the procedure so that it can be straightforwardly applied to new data and new languages, other than English (Ruette, Ehret & Szmrecsanyi 2016), Dutch (Geeraerts, Grondelaers & Speelman 1999) and Portuguese (Soares da Silva 2010). This paper can be characterized as a twofold extension of the previous studies. First, it aims to establish a general framework for lexical lectometry research that considers most if not all options for different steps. Second, we want to go beyond the Indo-European languages by extending the framework on a typologically unrelated language, i.e. Chinese varieties. For the general framework, we propose that a proper lexical lectometry research normally should involve the following steps: (1) compilation of a lectally stratified corpus; (2) sampling concepts as measuring points for lectometry; (3) identification of lexical expressions per concept; (4) disambiguation of lexical expressions in corpus data; (5) calculation of aggregated lexico-lectometric distances; (6) evaluation of measurement reliability and validity. For each step, we further provide possible options and caveats. For instance, step 2 and 3 can rely on existing concept-based lexical databases, like a synonym dictionary, or use corpus-driven keyword extraction and semantic vector space models. Step 4 can either make use of token-level distributional semantics models or rely on simpler n-gram language models. To assess the portability of the general framework, both in practical and linguistic-typological terms, we perform a lexical lectometric analysis for varieties of Chinese based on data from large-scale corpora of Mainland Chinese, Taiwan Chinese and Singapore Chinese.

We conduct a corpus study to investigate the structure of multi-word expressions (MWEs) in the clinical domain. Based on an existing medical taxonomy, we develop an annotation scheme and label a sample of MWEs from a Dutch corpus with semantic and grammatical features. The analysis of the annotated data shows that the formal structure of clinical MWEs correlates with their conceptual properties. The insights gained from this study could inform the design of Natural Language Processing (NLP) systems for clinical writing, but also for other specialized genres. no ISSN ispartof: pages:18-29 ispartof: Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018) pages:18-29 ispartof: Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions co-located with COLING 2018 location:Santa Fe, NM date:25 Aug - 26 Aug 2018 status: Published online

In this study, we investigate which factors influence the linguistic distance of Catalan dialectal pronunciations from standard Catalan. We use pronunciations from three regions where the northwestern variety of the Catalan language is spoken (Catalonia, Aragon and Andorra). In contrast to Aragon, Catalan has an official status in both Catalonia and Andorra, which likely influences standardization. Because we are interested in the potentially large range of differences that standardization might promote, we examine 357 words in Catalan varieties and in particular their pronunciation distances with respect to the standard. In order to be sensitive to differences among the words, we fit a generalized additive mixed-effects regression model to this data. This allows us to examine simultaneously the general (i.e. aggregate) patterns in pronunciation distance and to detect those words that diverge substantially from the general pattern. The results reveal higher pronunciation distances from standard Catalan in Aragon than in the other regions. Furthermore, speakers in Catalonia and Andorra, but not in Aragon, show a clear standardization pattern, with younger speakers having dialectal pronunciations closer to the standard than older speakers. This clearly indicates the presence of a border effect within a single country with respect to word pronunciation distances. Since a great deal of scholarship focuses on single segment changes, we compare our analysis to the analysis of three segment changes that have been discussed in the literature on Catalan. This comparison shows that the pattern observed at the word pronunciation level is supported by two of the three cases examined. As not all individual cases conform to the general pattern, the aggregate approach is necessary to detect global standardization patterns.

Distributional semantic models have shown to be a successful technique for Word Sense Disambiguation and Word Sense Induction tasks. However, these models, and more specifically the token-level variants, are extremely parameter-rich. We are still in the dark on how the different parameters can be efficiently set and even more on how to evaluate the outcome when no gold standard is readily available. To gain a better insight, we are developing a visual analytics approach which shows these models in two ways: a scatterplot matrix for inter-model parameter comparison and zoomable individual scatter plots allowing for more details on-demand. More specifically, we first use a scatterplot matrix to compare models with different parameter settings in a single view. This enables us to track selections of tokens over different models. On top of this, we create a scatter plot for each individual model, enriched with both model dependent and model independent features. This way, we can have a more in-depth visual analysis of what is going on and visualise the distinct properties or parameters of the individual model. ispartof: pages:24-29 ispartof: Proceedings of the LREC 2016 Workshop: VisLR II: Visualization as Added Value in the Development, Use and Evaluation of Language Resources pages:24-29 ispartof: VISLR location:Portorož, Slovenia date:23 May - 23 May 2016 status: published

Résumé. De plus en plus, de grandes collections de documents bilingues sont disponibles en ligne, particulièrement dans le domaine juridique. Ce type de mégadonnées constitue une ressource précieuse que les traducteurs spécialisés exploitent pour chercher des exemples informatifs sur la traduction et l’usage des expressions terminologiques. Cependant, il leur manque des outils pour récupérer les traductions les plus pertinentes. Nous présentons TermWise, un projet de cooperation entre terminologues, linguistes de corpus et informaticiens, qui visait à tirer profit de la grande collection de traductions disponibles en ligne en vue d’offrir un appui terminologique pour les traducteurs juridiques auprès du Service Public Fédéral de la Justice belge. Le projet a développé des algorithmes d’extraction de connaissances à partir de données textuelles bilingues et les a ensuite intégrés dans un outil cloud qui offre les traductions des expressions spécialisées les plus pertinentes relatives au texte source. Abstract. Increasingly, large bilingual document collections are being made available online, especially in the legal domain. This type of Big Data is a valuable resource that specialized translators exploit to search for informative examples of how domain-specific expressions should be translated. However, they lack the tools to retrieve previous translations that are maximally relevant. In this paper, we report on the TermWise project, a cooperation of terminologists, corpus linguists and computer scientists, that aims to leverage big online translation data for terminological support to legal translators at the Belgian Federal Ministry of Justice. The project developed dedicated knowledge extraction algorithms and a serverbased tool to provide translators with the most relevant previous translations of domain-specific expressions relative to the current translation assignment. Mots-clés : Terminologie, outil corpus, traduction. Keywords: Legal Terminology, corpus tool, translation. Geen ISBN ispartof: pages:1-5 ispartof: TAO-CAT: proceedings pages:1-5 ispartof: TAO-CAT location:Angers, France date:18 Jun - 20 Jun 2015 status: published

Geen ISBN ispartof: pages:17-19 ispartof: Proceedings of Quantitative Investigations in Theoretical Linguistics 5 pages:17-19 ispartof: Quantitative Investigations in Theoretical Linguistics (QITL) location:Leuven, Belgium date:12 Sep - 14 Sep 2013 status: published

This contribution seeks to connect usage based linguistics with dynamic systems theory, in particular as applied by Edelman (1989) and Thelen and Smith (1994). Edelman’s dynamic biological system starts off with a few simple sub-systems (perception, action, value), all of which interacting with each other over time, that give rise to perceptual categorization and higher order thinking and behavior through interaction with other humans and the environment. As Thelen and Smith argue, humans may be genetically predisposed to certain behavior, but they can develop only through actual experience. If we accept such a dynamic view of development, the idea of predetermined development in step-like stages makes no sense. There are stages in that some sub-systems will have to develop and be coordinated before those of a higher level can be reached (also called a precursor relation), but the pathway is not neatly paved for the child: the child needs to learn through his own experience with lots of ups and downs and development is individually owned. Like other complex adaptive systems, a human infant is made up of numerous interconnected subsystems at different levels, which have a tendency to self-organize and therefore will show nonlinear patterns of development. The benefit of taking a dynamic perspective on human development is that naturally feeds into a usage based view of language development: human cognition and experience are intricately related with the way language has emerged and used.

Increasingly, large bilingual document collections are being made available online, especially in the legal domain. This type of Big Data is a valuable resource that specialized translators exploit to search for informative examples of how domain-specific expressions should be translated. However, general purpose search engines are not optimized to retrieve previous translations that are maximally relevant to a translator. In this paper, we report on the TermWise project, a cooperation of terminologists, corpus linguists and computer scientists, that aims to leverage big online translation data for terminological support to legal translators at the Belgian Federal Ministry of Justice. The project developed dedicated knowledge extraction algorithms and a server-based tool to provide translators with the most relevant previous translations of domain-specific expressions relative to the current translation assignment. In the paper, we give an overview of the system, give a demo of the user interface and then discuss, more in general, the possibilities of mining big data to support specialized translation. Geen ISBN/ISSN ispartof: pages:167-176 ispartof: Proceedings of the 11th Terminology and Knowledge Engineering Conference: Ontology, Terminology & Text Mining (TKE) (online) pages:167-176 ispartof: Terminology and Knowledge Engineering Conference: Ontology, Terminology & Text Mining (TKE) location:Berlin, Germany date:19 Jun - 21 Jun 2014 status: published

Increasingly, large bilingual document collections are being made available online, especially in the legal domain. This type of Big Data is a valuable resource that specialized translators exploit to search for informative examples of how domain-specific expressions should be translated. However, general purpose search engines are not optimized to retrieve previous translations that are maximally relevant to a translator. In this paper, we report on the TermWise project, a cooperation of terminologists, corpus linguists and computer scientists, that aims to leverage big online translation data for terminological support to legal translators at the Belgian Federal Ministry of Justice. The project developed dedicated knowledge extraction algorithms and a server-based tool to provide translators with the most relevant previous translations of domain-specific expressions relative to the current translation assignment. The functionality is implemented as an extra database, a Term&Phrase Memory, that is meant to be integrated with existing Computer Assisted Translation tools. In the paper, we give an overview of the system, give a demo of the user interface, we present a user-based evaluation by translators and discuss how the tool is part of the general evolution towards exploiting Big Data in translation. ispartof: pages:4018-4022 ispartof: Proceedings of the 9th International Conference on Language Resources and Evaluation Conference (LREC) pages:4018-4022 ispartof: Language Resources and Evaluation Conference (LREC) location:Reykjavik, Iceland date:26 May - 31 May 2014 status: published

Within Computational Linguistics, distributional models of semantics have become the mainstay of large-scale modelling of lexical semantics. Distributional modelling also holds a large potential for research in Linguistics proper : It allows linguists to base their analysis on large amounts of usage data, thus vastly extending their empirical basis, and makes it possible to detect potentially interesting semantic patterns. However, so far, there have been relatively few applications, mainly because of the technical complexity and the lack of a linguist-friendly interface to explore the output. In this paper, we propose an interactive visualization of a distributional similarity matrix based on Multi-Dimensional Scaling. We present our prototype for a visualization tool built in Processing which opens up new possibilities for the visual analysis of token-based models and apply it to a small case study of a Dutch polysemous word. ispartof: pages:154-166 ispartof: Actes de SemDis 2013 : Enjeux actuels de la sémantique distributionnelle pages:154-166 ispartof: Traitement Automatique du Langage Naturel location:Les Sables-d'Olonne date:17 Jun - 21 Jun 2013 status: published

Many approaches to term extraction focus on the extraction of multiword units, assuming that multiword units comprise the majority of terms in most subject fields. However, this supposed prevalence of multiword terms has gone largely untested in the literature. In this paper, we perform a quantitative corpus-based analysis of the claim that multiword units are more technical than single word units, and that multiword units are more widespread in specialized domains. As a case study, we look at Dutch terminology from the Belgian legal domain. First, the relevant units are extracted using linguistic filters and an algorithm to identify Dutch compounds and multiword units. In a second step, we calculate for all units an association measure that captures the degree to which a linguistic unit belongs to the domain. Thirdly, we analyze the relationship between the units' technicality, frequency and their status as a simplex, compound or multiword unit. ispartof: pages:283-290 ispartof: Proceedings of the 10th Terminology and Knowledge Engineering Conference (TKE2012): New frontiers in the constructive symbiosis of terminology and knowledge engineering pages:283-290 ispartof: Terminology and Knowledge Engineering Conference (TKE2012): New frontiers in the constructive symbiosis of terminology and knowledge engineering location:Madrid date:19 Jun - 22 Jun 2012 status: published

In statistical NLP, Semantic Vector Spaces (SVS) are the standard technique for the automatic modeling of lexical semantics. However, it is largely unclear how these black-box techniques exactly capture word meaning. To explore the way an SVS structures the individual occurrences of words, we use a non-parametric MDS solution of a token-by-token similarity matrix. The MDS solution is visualized in an interactive plot with the Google Chart Tools. As a case study, we look at the occurrences of 476 Dutch nouns grouped in 214 synsets. ispartof: pages:16-24 ispartof: Proceedings of the EACL-2012 joint workshop of LINGVIS & UNCLH: Visualization of Language Patters and Uncovering Language History from Multilingual Resources pages:16-24 ispartof: European Chapter of the Association for Computational Linguistics location:Avignon date:23 Apr - 27 Apr 2012 status: published