Sketch Engine for Bilingual Lexicography

Kovář, Vojtěch; Baisa, Vít; Jakubíček, Miloš

doi:10.1093/ijl/ecw029

Cited by 15 publications

(10 citation statements)

References 2 publications

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“…For details and analysis of other kinds of modifiers, see[5] 10. Hypothesis is consistent with the training data, i.e.…”

supporting

confidence: 61%

“…In our method we try to find the description of all plausible hypotheses that are consistent with the training data and are derivable from the provided examples. 10 To this end we assume that there is no noise in the training data [14]. In other words, the examples supplied to the learner are adequate for the prediction of the refined concept.…”

Section: Refining Hypothesis Spacementioning

confidence: 99%

“…For details see, IAU International Astronomical Union 0603 Press Release: https://www.iau.org/news/pressreleases/detail/iau0603/ 2 For details, see [10], [12]. 3 See, for instance, [9].…”

Section: Introductionmentioning

confidence: 99%

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Refining Concepts by Machine Learning

Menšík¹,

Duží²,

Albert³

et al. 2019

CyS

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In this paper we deal with machine learning methods and algorithms applied in learning simple concepts by their refining or explication. The method of refining a simple concept of an object O consists in discovering a molecular concept that defines the same or a very similar object to the object O. Typically, such a molecular concept is a professional definition of the object, for instance a biological definition according to taxonomy, or legal definition of roles, acts, etc. Our background theory is Transparent Intensional Logic (TIL). In TIL concepts are explicated as abstract procedures encoded by natural language terms. These procedures are defined as six kinds of TIL constructions. First, we briefly introduce the method of learning with a supervisor that is applied in our case. Then we describe the algorithm 'Framework' together with heuristic methods applied by it. The heuristics is based on a plausible supply of positive and negative (near-miss) examples by which learner's hypotheses are refined and adjusted. Given a positive example, the learner refines the hypothesis learnt so far, while a near-miss example triggers specialization. Our heuristic methods deal with the way refinement is applied, which includes also its special cases generalization and specialization.

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“…For details and analysis of other kinds of modifiers, see[5] 10. Hypothesis is consistent with the training data, i.e.…”

supporting

confidence: 61%

Section: Refining Hypothesis Spacementioning

confidence: 99%

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Refining Concepts by Machine Learning

Menšík¹,

Duží²,

Albert³

et al. 2019

CyS

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“…Besides, they can be used to evaluate different approaches for the classification of web documents and content by text genre and topic area (e.g., (Chouigui et al, 2017)). Furthermore, web corpora have become a prime and well-established source for lexicographers to create many large and various dictionaries using specialised tools such as the corpus query and corpus management tool Sketch-Engine (Kovář et al, 2016). Moreover, some completely new areas of research, for which they deal exclusively with web corpora, have emerged.…”

Section: Literature Reviewmentioning

confidence: 99%

OSIAN: Open Source International Arabic News Corpus - Preparation and Integration into the CLARIN-infrastructure

Zeroual¹,

Goldhahn

Eckart

et al. 2019

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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The World Wide Web has become a fundamental resource for building large text corpora. Broadcasting platforms such as news websites are rich sources of data regarding diverse topics and form a valuable foundation for research. The Arabic language is extensively utilized on the Web. Still, Arabic is relatively an under-resourced language in terms of availability of freely annotated corpora. This paper presents the first version of the Open Source International Arabic News (OSIAN) corpus. The corpus data was collected from international Arabic news websites, all being freely available on the Web. The corpus consists of about 3.5 million articles comprising more than 37 million sentences and roughly 1 billion tokens. It is encoded in XML; each article is annotated with metadata information. Moreover, each word is annotated with lemma and part-ofspeech. The described corpus is processed, archived and published into the CLARIN infrastructure. This publication includes descriptive metadata via OAI-PMH, direct access to the plain text material (available under Creative Commons Attribution-Non-Commercial 4.0 International License-CC BY-NC 4.0), and integration into the WebLicht annotation platform and CLARIN's Federated Content Search FCS.

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“…Twi rather has some standard literature like dictionaries, the Holy Bible, story books and published articles on aspects of the language freely available online and offline but it has no standard curated, annotated and POS tagged corpus. It is refreshing however, to note that some researchers like [8,29] initiated a bold move to create a language dataset and resources for the Low Resource African Languages (LRALs) but the project has been stalled. Considering the fact that any MT model would need enormous data in the form of parallel corpora to train the model which is conspicuously missing for Twi.…”

Section: Introductionmentioning

confidence: 99%

TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language

Afram¹,

Weyori²,

Adekoya³

2022

Preprint

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A Twi-English parallel corpus is certainly an important resource for Machine Translation of Twi (ISO 639-3), a Low-Resource African Language (LRAL) which is mainly spoken in Ghana and Ivory Coast. Currently large-scale multi-domain Twi-English parallel corpus is still unavailable partly due to the difficulties and the arduous efforts required in its design. In this paper, we present TWIENG: a large-scale multi-domain Twi-English parallel corpus. We crawled the sentences from the web using web crawlers, translated, aligned, tokenized and compiled to create the corpus. We crawled English sentences from Ghanaian indigenous electronic news portals, Ghanaian Parliamentary Hansards, Twi Bible and crowdsourcing via google forms. The sentences were translated by professional translators and linguists, they were then aligned, tokenized and compiled. The corpus was curated using the sketch engine, a corpus manager and analysis software developed by Lexical Computing Limited. The corpus was manually evaluated by Twi professional linguists. The Corpus has 5,419 parallel sentences.

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Sketch Engine for Bilingual Lexicography

Cited by 15 publications

References 2 publications

Refining Concepts by Machine Learning

Refining Concepts by Machine Learning

OSIAN: Open Source International Arabic News Corpus - Preparation and Integration into the CLARIN-infrastructure

TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language

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