All-words word sense disambiguation for Turkish

Acikgoz, Onur; Gurkan, Ali Tunca; Ertopcu, Burak; Topsakal, Ozan; Özenç, Berke; Kanburoglu, Ali Bugra; Cam, Ilker; Avar, Begüm; Ercan, Gokhan; Yıldız, Olcay Taner

doi:10.1109/ubmk.2017.8093442

Cited by 4 publications

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“…Açıkgöz et al [3] studied on semantic ambiguity for Turkish language and aimed to measure the effect of the performance of the given features for different classification algorithms. A randomly obtained dataset from Penn Treebag Corpus was translated into Turkish, and there were 1400 sentences in the translated dataset.…”

Section: Related Work About Word Sense Disambiguationmentioning

confidence: 99%

“…While the studies on WSD problem were mostly done in English, we conducted this study for Turkish language, which is harder to study due to the complexity of Turkish morphology, syntactic structure, and being an agglutinative language. In the literature, there are some studies on WSD for Turkish language, for example: Açıkgöz et al [3] studied on semantic ambiguity for Turkish language and measured the performance of the given features for different classification algorithms. Orhan and Altan [4], conducted experiments to find effective features to eliminate the ambiguity of meaning in Turkish verbs and summarized the results.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Performance of Sentiment Analysis by Ensemble Hybrid Learning Algorithm With NLP And Cascaded Feature Extraction

Altınel

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2023

International Journal of Advances in Engineering and Pure Sciences

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Sentiment analysis is a challenging problem in Natural Language Processing since every language has its own character within several difficulties such as ambiguity, synonymy, negative suffixes…etc. Since words with ambiguity can have different sentiment scores depending on the meaning they have in their corresponding context, we accomplished a study on Turkish language to determine whether the polarity scores of these polysemous words may differ according to their meaning. For a word with ambiguity, we first made a polarity calculation module to calculate its polarity score. This way, we calculated the polarity scores of 100 Turkish polysemous words. Then, since negation directly affects the correct meaning of the word in the sentiment analysis, a negation handler module is also implemented. After that, we prepared a sentiment polarity corpus which consists of 159,876 Turkish words including 100 Turkish polysemous words. Actually, the main purpose of this study is to detect sentiment polarity of Turkish texts by considering and building a specialized module for polysemous words. In short, we built a system for Turkish sentiment polarity detection task including these modules: 1) Pre-processing, 2) Polarity Calculation Module, 3) Negation Handling Module, 4) Feature Generation Module, and 5) Classification Module. According to our knowledge, this is the first study which includes all of these modules in one Turkish sentiment analysis task. Finally, we conducted this corpus using an ensemble hybrid regularized learning algorithm on two self-collected Twitter-datasets. Experimental results show that the suggested approach improves the classification performance on Turkish sentiment analysis task.

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Section: Related Work About Word Sense Disambiguationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%