A Constraint Approach to Pivot-Based Bilingual Dictionary Induction

2016 ◽  
Vol 15 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Mairidan Wushouer ◽  
Donghui Lin ◽  
Toru Ishida ◽  
Katsutoshi Hirayama
Keyword(s):  
Bilingual Dictionary ◽  
Constraint Approach

Effect of Social Norm on Consumer Demand: Multiple Constraint Approach

2020 ◽  
Vol 22 (1) ◽  
pp. 41-60
Author(s):  
Sungjee Choi ◽  
Inwoo Nam ◽  
Jaehwan Kim
Keyword(s):  
Social Norm ◽  
Consumer Demand ◽  
Multiple Constraint ◽  
Constraint Approach

Parsing and Tagging of Bilingual Dictionary

2003 ◽  
Author(s):  
Huanfeng Ma ◽  
Burcu Karagol-Ayan ◽  
David Doermann ◽  
Doug Oard ◽  
Jianqiang Wang
Keyword(s):  
Bilingual Dictionary

Developing the Persian Wordnet of Verbs Using Supervised Learning

2021 ◽  
Vol 20 (4) ◽  
pp. 1-18
Author(s):  
Zahra Mousavi ◽  
Heshaam Faili
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Language Processing ◽  
Supervised Classification ◽  
Word Sense ◽  
Direct Influence ◽  
Training Set ◽  
Bilingual Dictionary ◽  
Automated Method ◽  
Princeton Wordnet

Nowadays, wordnets are extensively used as a major resource in natural language processing and information retrieval tasks. Therefore, the accuracy of wordnets has a direct influence on the performance of the involved applications. This paper presents a fully-automated method for extending a previously developed Persian wordnet to cover more comprehensive and accurate verbal entries. At first, by using a bilingual dictionary, some Persian verbs are linked to Princeton WordNet synsets. A feature set related to the semantic behavior of compound verbs as the majority of Persian verbs is proposed. This feature set is employed in a supervised classification system to select the proper links for inclusion in the wordnet. We also benefit from a pre-existing Persian wordnet, FarsNet, and a similarity-based method to produce a training set. This is the largest automatically developed Persian wordnet with more than 27,000 words, 28,000 PWN synsets and 67,000 word-sense pairs that substantially outperforms the previous Persian wordnet with about 16,000 words, 22,000 PWN synsets and 38,000 word-sense pairs.

A hierarchical constraint approach for dynamic modeling and trajectory tracking control of a mobile robot

2021 ◽  
pp. 107754632199918
Author(s):  
Rongrong Yu ◽  
Shuhui Ding ◽  
Heqiang Tian ◽  
Ye-Hwa Chen
Keyword(s):  
Mobile Robot ◽  
Dynamic Modeling ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Wheeled Mobile Robot ◽  
Structural Constraints ◽  
Control Force ◽  
Trajectory Tracking Control ◽  
Motion Constraints ◽  
Constraint Approach

The dynamic modeling and trajectory tracking control of a mobile robot is handled by a hierarchical constraint approach in this study. When the wheeled mobile robot with complex generalized coordinates has structural constraints and motion constraints, the number of constraints is large and the properties of them are different. Therefore, it is difficult to get the dynamic model and trajectory tracking control force of the wheeled mobile robot at the same time. To solve the aforementioned problem, a creative hierarchical constraint approach based on the Udwadia–Kalaba theory is proposed. In this approach, constraints are classified into two levels, structural constraints are the first level and motion constraints are the second level. In the second level constraint, arbitrary initial conditions may cause the trajectory to diverge. Thus, we propose the asymptotic convergence criterion to deal with it. Then, the analytical dynamic equation and trajectory tracking control force of the wheeled mobile robot can be obtained simultaneously. To verify the effectiveness and accuracy of this methodology, a numerical simulation of a three-wheeled mobile robot is carried out.

Kulturell geprägte Wörter zwischen sprachlicher Äquivalenz und kultureller Kompetenz. Am Beispiel deutsch-italienischer Wörterbücher

2020 ◽  
Vol 36 (1) ◽  
pp. 181-204
Author(s):  
Martina Nied Curcio
Keyword(s):  
Intercultural Competence ◽  
Cultural Knowledge ◽  
Bilingual Dictionary ◽  
Bilingual Dictionaries ◽  
Linguistic Level

AbstractMisunderstandings between speakers of different languages occur not only on a linguistic level but also on a cultural one. Consultation of a bilingual dictionary does not necessarily help in this case, as information on the cultural level is often missing. In this paper we will discuss how bilingual dictionaries can draw attention to cultural divergences so that the dictionary user acquires cultural knowledge and is able to build an intercultural competence. Examples from four bilingual dictionaries (German-Italian) are given to illustrate how culture-bound words are represented. For this purpose, a classification of culture-bound words is offered. Finally, the prerequisites and possibilities of an appropriate representation of culture-bound items in bilingual dictionaries will be proposed.

scholarly journals Generation of Cross-Lingual Word Vectors for Low-Resourced Languages Using Deep Learning and Topological Metrics in a Data-Efficient Way

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1372
Author(s):  
Sanjanasri JP ◽  
Vijay Krishna Menon ◽  
Soman KP ◽  
Rajendran S ◽  
Agnieszka Wolk
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Semantic Space ◽  
Semantic Interpretation ◽  
Learning Approaches ◽  
Qualitative Comparison ◽  
Bilingual Dictionary ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Cross Lingual

Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.

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