An Integrated Approach to Automatic Synonym Detection in Turkish Corpus

Yıldız, Tuğba; Yıldırım, Savaş; Di̇ri̇, Banu

doi:10.1007/978-3-319-10888-9_12

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…In [31], only 206 out of 66K synonym relations were taken into consideration with an 88% success rate. In our previous [32], the F-measure was 80.3%. In the current study, we used a variety of features obtained from multiple sources and the success rate was 95.2%.…”

Section: Resultsmentioning

confidence: 85%

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Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Yıldız¹,

Di̇ri̇²,

Yıldırım³

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this study, a model is proposed to determine synonymy by incorporating several resources. The model extracts the features from monolingual online dictionaries, a bilingual online dictionary, WordNet, and a monolingual Turkish corpus. Once it has built a candidate list, it determines the synonymy for a given word by means of those features. All these resources and the approaches are evaluated. Taking all features into account and applying machine learning algorithms, the model shows good performance of F-measure with 81.4%. The study contributes to the literature by integrating several resources and attempting the first corpus-driven synonym detection system for Turkish.

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Section: Resultsmentioning

confidence: 85%

“…As another attempt to detect synonyms in Turkish [32], a corpus-driven distributional similarity model was proposed based on only features, i.e. dependency relations and semantic features obtained by syntactic patterns and lexicosyntactic patterns (LSPs), respectively.…”

Section: Introductionmentioning

confidence: 99%

Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Yıldız¹,

Di̇ri̇²,

Yıldırım³

2017

Turk J Elec Eng & Comp Sci

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“…To perform NLP tasks, it is necessary to consider the knowledge of synonyms and different ways of naming the same object or phenomenon, especially in high-level assignments that mimic human dialogue. One problem with language-dependent approaches is that synonyms are defined for a target language, e.g., recognition of synonyms in Turkish [56], use of WordNet [57] to obtain a synonym for the extraction of event information from social text streams [58], and such approach cannot be easily applied to low resource languages.…”

Section: Challenges and Issues With Language Dependent Nlpmentioning

confidence: 99%

Detection of Radicalisation and Extremism Online: A Survey

Khan

Pais

Cordeiro

et al. 2022

Preprint

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Introduction: Due to the lack of regulation, the large volume of user-generated online content reflects more closely the offline world than official news sources. Therefore, social media platforms have become an attractive space for anyone seeking independent information. One of the main goals of this work is to clarify concepts such as Extremism and Collective Radicalisation, Social Media, Sentiments/Emotions/Opinions Analysis, as well as the combinations of all of them. Methods: The automatic identification of extremism and collective radicalisation requires sophisticated Natural Language Processing (NLP) methods and resources, especially those dealing with opinions, emotions or sentiment analysis. Text mining and knowledge extraction are also crucial, in particular, directed toward social media and micro-blogging. Results: The present document comprehends a study on theoretical material, focusing on the main concepts of the subject, including the main problems and challenges, from the different areas that compose online radicalisation research. Understanding and detecting extremism and collective radicalism online has a connection to sentiment analysis and opinion mining. There are many barriers to understanding extremism and collective radicalisation; one is to differentiate between who is really engaged in the process and who is just eventually talking about it. Conclusions: The other focus of this work is to find the best ways to identify extremism and collective radicalisation on the internet, using sentiment analysis and focusing on probabilistic methods to create an unsupervised and language-independent approach.

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“…For Turkish, there are some studies to extract semantic relation pairs from corpus and dictionary definitions [7], [8]. Hyponym-hypernym [9], [10], meronym-holonym [11] and synonym [12] pairs have been automatically extracted from Turkish corpus. For hyponym-hypernym, meronym-holonym and synonym pairs 83.0%, 75.0% and 80.3% accuracies were obtained, respectively.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Lexico-Syntactic Patterns for Antonym Pair Extraction from a Turkish Corpus

Sahin

Di̇ri̇

Yıldız

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Extraction of semantic relations from various sources such as corpus, web pages, dictionary definitions etc. is one of the most important issue in study of Natural Language Processing (NLP). Various methods have been used to extract semantic relation from various sources. Pattern-based approach is one of the most popular method among them. In this study, we propose a model to extract antonym pairs from Turkish corpus automatically. Using a set of seeds, we automatically extract lexico-syntactic patterns (LSPs) for antonym relation from corpus. Reliability score is calculated for each pattern. The most reliable patterns are used to generate new antonym pairs. Study conduct on only adjective-adjective and noun-noun pairs. Noun and adjective target words are used to measure success of method and candidate antonyms are generated using reliable patterns. For each antonym pair consisting of candidate antonym and target word, antonym score is calculated. Pairs that have a certain score are assigned to antonym pair. The proposed method shows good performance with 77.2% average accuracy.

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An Integrated Approach to Automatic Synonym Detection in Turkish Corpus

Cited by 9 publications

References 27 publications

Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Detection of Radicalisation and Extremism Online: A Survey

Analysis of Lexico-Syntactic Patterns for Antonym Pair Extraction from a Turkish Corpus

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