scholarly journals Extraction of Terms Related to Named Rivers

Languages ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 46
Author(s):  
Juan ◽  
Faber
Keyword(s):  
Knowledge Base ◽  
Mississippi River ◽  
Environmental Science ◽  
Automatic Method ◽  
Statistical Procedures ◽  
Conceptual Structures ◽  
Semantic Models ◽  
Semantic Frames ◽  
Conceptual Network ◽  
Distributional Semantic Models

EcoLexicon is a terminological knowledge base on environmental science, whose design permits the geographic contextualization of data. For the geographic contextualization of landform concepts, this paper presents a semi-automatic method for extracting terms associated with named rivers (e.g., Mississippi River). Terms were extracted from a specialized corpus, where named rivers were automatically identified. Statistical procedures were applied for selecting both terms and rivers in distributional semantic models to construct the conceptual structures underlying the usage of named rivers. The rivers sharing associated terms were also clustered and represented in the same conceptual network. The results showed that the method successfully described the semantic frames of named rivers with explanatory adequacy, according to the premises of Frame-Based Terminology.

Detecting linguistic idiosyncratic interests in autism using distributional semantic models

2014 ◽  
Author(s):  
Masoud Rouhizadeh ◽  
Emily Prud'hommeaux ◽  
Jan van Santen ◽  
Richard Sproat
Keyword(s):  
Semantic Models ◽  
Distributional Semantic Models

scholarly journals Unsupervised Compositionality Prediction of Nominal Compounds

2019 ◽  
Vol 45 (1) ◽  
pp. 1-57 ◽  
Author(s):  
Silvio Cordeiro ◽  
Aline Villavicencio ◽  
Marco Idiart ◽  
Carlos Ramisch
Keyword(s):  
Morphological Variation ◽  
Red Wine ◽  
Data Sets ◽  
High Agreement ◽  
Semantic Models ◽  
Corpus Size ◽  
Nominal Compounds ◽  
Distributional Semantic Models ◽  
The Impact

Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.

EcoLexicon and by-products

Terminology ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 222-258 ◽  
Author(s):  
Pilar León-Araúz ◽  
Arianne Reimerink ◽  
Pamela Faber
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Visual Information ◽  
Environmental Science ◽  
Image Annotation ◽  
Open Data ◽  
Annotation Tool ◽  
Different Types ◽  
By Products

Abstract Reutilization and interoperability are major issues in the fields of knowledge representation and extraction, as reflected in initiatives such as the Semantic Web and the Linked Open Data Cloud. This paper shows how terminological resources can be integrated and reused within different types of application. EcoLexicon is a multilingual terminological knowledge base (TKB) on environmental science that integrates conceptual, linguistic and visual information. It has led to the following by-products: (i) the EcoLexicon English Corpus; (ii) EcoLexiCAT, a terminology-enhanced translation tool; and (iii) Manzanilla, an image annotation tool. This paper explains EcoLexicon and its by-products, and shows how the latter exploit and enhance the data in the TKB.

Exploiting Distributional Semantic Models in Question Answering

2012 ◽  
Author(s):  
Piero Molino ◽  
Pierpaolo Basile ◽  
Annalina Caputo ◽  
Pasquale Lops ◽  
Giovanni Semeraro
Keyword(s):  
Question Answering ◽  
Semantic Models ◽  
Distributional Semantic Models

scholarly journals A critique of word similarity as a method for evaluating distributional semantic models

2016 ◽  
Author(s):  
Miroslav Batchkarov ◽  
Thomas Kober ◽  
Jeremy Reffin ◽  
Julie Weeds ◽  
David Weir
Keyword(s):  
Word Similarity ◽  
Semantic Models ◽  
Distributional Semantic Models

scholarly journals EVALution 1.0: an Evolving Semantic Dataset for Training and Evaluation of Distributional Semantic Models

2015 ◽  
Author(s):  
Enrico Santus ◽  
Frances Yung ◽  
Alessandro Lenci ◽  
Chu-Ren Huang
Keyword(s):  
Semantic Models ◽  
Distributional Semantic Models
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