Parsing and Tagging of Bilingual Dictionary

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.

Download Full-text

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

Lexicographica - International Annual for Lexicography / Internationales Jahrbuch für Lexikographie ◽

10.1515/lex-2020-0010 ◽

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.

Download Full-text

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

Electronics ◽

10.3390/electronics10121372 ◽

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.

Download Full-text

Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

Applied Sciences ◽

10.3390/app11156846 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6846

Author(s):

Kashish Ara Shakil ◽

Kahkashan Tabassum ◽

Fawziah S. Alqahtani ◽

Mudasir Ahmad Wani

Keyword(s):

Saudi Arabia ◽

Behavioral Differences ◽

Initial Attempt ◽

Bilingual Dictionary ◽

Metropolitan Cities ◽

Emotion Lexicon ◽

User Data ◽

Regional Location ◽

Media Platform ◽

Holy City

Humans are the product of what society and their environment conditions them into being. People living in metropolitan cities have a very fast-paced life and are constantly exposed to different situations. A social media platform enables individuals to express their emotions and sentiments and thus acts as a reservoir for the digital emotion footprints of its users. This study proposes that the user data available on Twitter has the potential to showcase the contrasting emotions of people residing in a pilgrimage city versus those residing in other, non-pilgrimage areas. We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city). The user emotions were classified on the basis of Plutchik’s eight basic emotion categories, Fear, Anger, Sadness, Joy, Surprise, Disgust, Trust, and Anticipation. A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons. An emotion analysis revealed that people living in Mecca had more positivity than those residing in Riyadh. Anticipation was the emotion that was dominant or most expressed in both places. However, a larger proportion of users living in Mecca fell under this category. The proposed analysis was an initial attempt toward studying the emotional and behavioral differences between users living in different cities of Saudi Arabia. This study has several other important applications. First, the emotion-based study could contribute to the development of a machine learning-based model for predicting depression in netizens. Second, behavioral appearances mined from the text could benefit efforts to identify the regional location of a particular user.

Download Full-text