Lexical typology through similarity semantics: Toward a semantic map of motion verbs

Wälchli, Bernhard; Cysouw, Michael

doi:10.1515/ling-2012-0021

Cited by 71 publications

(83 citation statements)

References 35 publications

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“…To construct semantic maps directly from data extracted from multilingual parallel corpora, we apply an existing method in the lexical domain (Wälchli and Cysouw, 2012) at the level of grammar. We dub our method Translation Mining.…”

Section: Methodsmentioning

confidence: 99%

“…Hovering over a point on the map directly shows you the five-tuple the point is based on, and clicking on a point will yield a new page in which you can inspect the underlying data (see Figure 3 for an example). 5 Compared to Wälchli and Cysouw (2012), our main contributions in this methodology are (1) the web application to allow for easier annotation and (2) the interactive visualization of the MDS algo- 4 We use the MDS algorithm from the scikit-learn package (Pedregosa et al, 2011), a Python package for machine learning, and visualized the results using the nvd3 package (http://nvd3.org/). 5 The source code of this visualization is part of TimeAlign, see link above.…”

Section: Step 5) Visualization Via Multidimensional Scalingmentioning

confidence: 99%

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Mapping the Perfect via Translation Mining

Klis¹,

Bruyn²,

Swart³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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Semantic analyses of the PERFECT often defeat their own purpose: by restricting their attention to 'real' perfects (like the English one), they implicitly assume the PERFECT has predefined meanings and usages. We turn the tables and focus on form, using data extracted from multilingual parallel corpora to automatically generate semantic maps (Haspelmath, 1997) of the sequence 'HAVE/BE + past participle' in five European languages (German, English, Spanish, French, Dutch). This technique, which we dub Translation Mining, has been applied before in the lexical domain (Wälchli and Cysouw, 2012) but we showcase its application at the level of the grammar.

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Section: Methodsmentioning

confidence: 99%

Section: Step 5) Visualization Via Multidimensional Scalingmentioning

confidence: 99%

Mapping the Perfect via Translation Mining

Klis¹,

Bruyn²,

Swart³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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“…Typological features can also emerge in a data-driven fashion, based on distributional information from multi-parallel texts. Wälchli and Cysouw (2012) create a matrix where each row is a parallel sentence, each column is a language, and cell values are lemmas of motion verbs occurring in those sentences. This matrix can be transformed to a (Hamming) distance matrix between sentence pairs, and reduced to lower dimensionality via Multi-Dimensional Scaling (MDS).…”

Section: Heuristics Based On Cross-lingual Distributional Informationmentioning

confidence: 99%

“…Wälchli and Cysouw (2012)'s cross-lingual sentence visualization for Mapungundun. In the top-right corner is the legend of the motion verbs taken into consideration.…”

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confidence: 99%

Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

Ponti

O'Horan

Berzak

et al. 2019

Computational Linguistics

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Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by recent developments in data-driven induction of typological knowledge.

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“…Haspelmath (2016) for the latter) are not different in principle from our grammar-external comparative concepts (such as nonverbal stimuli in fieldwork experiments, or text passages in parallel texts, e.g., Wälchli & Cysouw (2012)), it will be easier to accept that language comparison is necessarily partial. Just as nonverbal stimuli and parallel text passages can never contain all possible concepts or situations, it will never be possible to compare languages with respect to all their features, and many features that are interesting to compare in some languages are necessarily uninteresting in many others.…”

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confidence: 97%

The challenge of making language description and comparison mutually beneficial

Haspelmath

2016

Linguistic Typology

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I argue that the distinction between comparative concepts and descriptive categories helps language describers and typologists to benefit from each other because describers are free to set up their own categories, typologists are free to define their own concepts, comparison need not involve complete systems, and interlinear translation can be either based on comparative concepts or descriptive categories. A similar distinction also exists in other disciplines that deal with cultural concepts.

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Lexical typology through similarity semantics: Toward a semantic map of motion verbs

Cited by 71 publications

References 35 publications

Mapping the Perfect via Translation Mining

Mapping the Perfect via Translation Mining

Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

The challenge of making language description and comparison mutually beneficial

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