The Romanian wordnet in a nutshell

Tufiș, Dan; Mititelu, Verginica Barbu; Ştefănescu, Dan; Ion, Radu

doi:10.1007/s10579-013-9230-7

Cited by 17 publications

(11 citation statements)

References 4 publications

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“…First, to address the former issue, RoWordNet [3], [15] must be introduced. This is the Romanian version of the Princeton WordNet, and it is a lexical database which allows the modeling of linguistic knowledge with regards to aspects of the real world.…”

Section: Related Workmentioning

confidence: 99%

“…As far as the Romanian language is concerned, the existence of RoWordNet [15], a large semantic network that encompasses information in the form of synsets (i.e. groups of full synonyms) linked by lexical-semantic relations, marks the central point of Romanian language processing research due to its size, completeness, and information richness.…”

Section: Introductionmentioning

confidence: 99%

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RoEmoLex - A Romanian Emotion Lexicon

Briciu

Lupea

2017

Studia UBB Informatica

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Abstract. In Natural Language Processing tasks, semantical and lexical resources are of paramount importance for efficient implementations of solutions. However, availability of tools for any other language than English is fairly limited, therefore leaving the field open to improvements and new developments. This paper presents the second version of RoEmoLex, a lexicon containing approximately eleven thousand Romanian words tagged with a series of emotions and two valences. We describe the steps followed in the improvement process of the first version of the resource: the addition of new terms, and the extension of emotional concepts to finer-grained tags.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RoEmoLex - A Romanian Emotion Lexicon

Briciu

Lupea

2017

Studia UBB Informatica

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“…The textual resources available in the corpus were initially available in different formats such as .doc and unprotected .pdf and they had to be converted into a raw text format in order to be annotated by our processing tools (Tufiş et al, 2008). The conversion of the files involved a boilerplate removal step in which footers, headers, page numbers, figures, tables, footnotes, etc.…”

Section: Corpus Structurementioning

confidence: 99%

Adapting the TTL Romanian POS Tagger to the Biomedical Domain

Mitrofan¹,

Ion²

2017

Proceedings of the Biomedical NLP Workshop

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This paper presents the adaptation of the Hidden Markov Models-based TTL partof-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97% when TTL's baseline model is updated with training information from a Romanian biomedical corpus. This corpus is developed in the context of the CoRoLa (a reference corpus for the contemporary Romanian language) project. Informative description and statistics of the Romanian biomedical corpus are also provided.

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“…According to [17], word-based diacritic restorations are often knowledge intensive, relying on the existence of linguistic resources like dictionaries, part-of-speech taggers, and morphological analyzers [24]. According to [23], the word-based model is often more appropriate for languages "where the change of diacritics has a grammatical or semantic role." Nevertheless, its major challenge is in handling Out of Vocabulary (OOV) or unknown words due to data sparsity [8,9].…”

Section: Word-level Restorationmentioning

confidence: 99%

Restoring Tone-Marks in Standard Yorùbá Electronic Text: Improved Model

Asahiah

Odejobi

Adagunodo

2017

csci

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Diacritic Restoration is a necessity in the processing of languages with Latinbased scripts that utilizes characters outside the basic Latin alphabet used by the English language. Yorùbá is one such language, marking an underdot (dotbelow) on three characters and tone marks on all seven vowels and two syllabic nasals. The problem of restoring underdotted characters has been fairly addressed using characters as linguistic units for restoration. However, the existing character-based approaches and word-based approach has not been able to sufficiently address the restoration of tone marks in Yorùbá. In this study, we address tone-mark restoration as a subset of diacritic restoration. We proposed using syllables derived from words as linguistic tokens for tone-mark restoration. In our experimental setup, we used Yorùbá text collected from various sources as data with a total word count of 250,336 words. These words, on syllabification, yielded 464,274 syllables. The syllables were divided into training and testing data in different proportions, ranging from 99% used for training and 1% used for testing to 70% used for training and 30% used for testing. The aim of evaluating the different proportions was to determine how the ratio of training-to-test data affected the variations that may occur in the result. We applied memory-based learning to train the models. We also set up a similar experiment using a character token to be able to compare the performance. The result showed that ,by using syllables, we were able to increase the wordlevel accuracy to 96.23% (an average of almost 15% over using characters). We also found that using 75% of the data for training and the remaining 25% for testing gives results with the least variation in a ten-fold cross validation test. Hybridizing this method that uses syllabless as processing linguistic units with other methods like lexicon lookup might likely lead to improvement over the current result.

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The Romanian wordnet in a nutshell

Cited by 17 publications

References 4 publications

RoEmoLex - A Romanian Emotion Lexicon

RoEmoLex - A Romanian Emotion Lexicon

Adapting the TTL Romanian POS Tagger to the Biomedical Domain

Restoring Tone-Marks in Standard Yorùbá Electronic Text: Improved Model

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