Development and Evaluation of Word Embeddings for Morphologically Rich Languages

Vasić, Daniel; Brajković, Emil

doi:10.23919/softcom.2018.8555822

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Table 2 below shows. Adjectives in the Albanian language have three forms: positive, comparative, and superlative [11]. Escalation is realized as a combination of the base word with the comparative article 'më', e.g.…”

Section: Part-of-speech (Pos) Taggingmentioning

confidence: 99%

“…The Albanian language has grammatical categories such as nouns, verbs, adjectives, numerals, genders and determinants, person-number indexing, tenses, active or passive voice. In grammar, a part of speech (also a word class, a lexical class, or a lexical category) is a linguistic category of words, which is generally defined by the syntactic or morphological action of the lexical item in question [11]. Tagging the www.aetic.theiaer.org Albanian language is especially challenging since it has extremely rich inflection paradigms and has 100 different forms: inflection patterns for levees of the same syntactic category.…”

Section: Introductionmentioning

confidence: 99%

“…They built on their own previous work to achieve around 70% accuracy. Also, recently, Kote et al [11] presented a corpus of 118,000 tokens tagged with part-of-speech and morphological features in Albanian. Furthermore, the team trained a neural morphological tagger and lemmatize that achieved good results, the best of which was 92.74% in POS tagging.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

Mati¹,

Hamiti²,

Susuri³

et al. 2021

AETiC

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The development of natural language processing resources for Albanian has grown steadily in recent years. This paper presents research conducted on unsupervised learning-the challenges associated with building a dictionary for the Albanian language and creating part-of-speech tagging models. The majority of languages have their own dictionary, but languages with low resources suffer from a lack of resources. It facilitates the sharing of information and services for users and whole communities through natural language processing. The experimentation corpora for the Albanian language includes 250K sentences from different disciplines, with a proposal for a part-of-speech tagging tag set that can adequately represent the underlying linguistic phenomena. Contributing to the development of Albanian is the purpose of this paper. The results of experiments with the Albanian language corpus revealed that its use of articles and pronouns resembles that of more high-resource languages. According to this study, the total expected frequency as a means for correctly tagging words has been proven effective for populating the Albanian language dictionary.

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Section: Part-of-speech (Pos) Taggingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

Mati¹,

Hamiti²,

Susuri³

et al. 2021

AETiC

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show abstract

“…The other reason is related to linguistic complexity. Croatian language is morphologically very rich, which increases the complexity in many areas of natural language processing [2]. Methods used in the case of English language cannot be directly applied because Croatian language is different in terms of orthography, phonetics and morphology.…”

Section: Introductionmentioning

confidence: 99%

N-gram Based Croatian Language Network: Application in a Smart Environment

Soic

Vuković

2022

JCOMSS

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In the field of natural language processing, language networks represent a method for observing linguistic units and their interactions in different linguistic contexts. This paper uses the previously presented Croatian language network for building a solution capable of generating spoken notifications in Croatian language. The novelty of this paper is that it proposes an approach for generating spoken notifications in smart environments by combining specialized services that enable interaction with the environment and human users. The process employed for generating spoken notifications is described in detail. Also, a novel contribution of this paper is the case-study evaluation of the proposed approach in a smart home environment.

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“…The second challenge is related to language modeling and cognitive processes. Unlike English, Croatian language is morphologically very rich [9]. This introduces many challenges related to speech recognition, but also cognitive analysis and synthesis, i.e.…”

Section: Introductionmentioning

confidence: 99%

Spoken notifications in smart environments using Croatian language

2021

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Speech technologies have advanced significantly in the last decade, mostly due to rise in available computing power combined with novel approaches to natural language processing. As a result, speech-enabled systems have become popular commercial products, successfully integrated with various environments. However, this can be stated for English and a few other ?big? languages. From the perspective of a minority language, such as Croatian, there are many challenges ahead to achieve comparable results. In this paper, we propose a model for natural language generation and speech synthesis in a smart environment using Croatian language. The model is evaluated on 27 users to estimate the quality of user experience. The evaluation goal was to determine what users perceive to be more important - generated speech quality or grammatical correctness of the spoken content. It is shown that most users perceived grammatically correct spoken texts as being of the highest quality.

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Development and Evaluation of Word Embeddings for Morphologically Rich Languages

Cited by 4 publications

References 8 publications

Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

N-gram Based Croatian Language Network: Application in a Smart Environment

Spoken notifications in smart environments using Croatian language

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