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    A Hybrid Method for Extracting Turkish Part-Whole Relation Pairs from Corpus
    (IEEE, 2016) Sahin, Gurkan; Diri, Banu; Yildiz, Tugba
    Extraction of various semantic relation pairs from different sources (dictionary definitions, corpus etc.) with high accuracy is one of the most popular topics in natural language processing (NLP). In this study, a hybrid method is proposed to extract Turkish part-whole pairs from corpus. Corpus statistics, WordNet similarities and Word2Vec word vector similarities are used together in this study. Firstly, initial part-whole seeds are prepared and by using these seeds part-whole patterns are extracted from corpus. For each pattern, a reliability score is calculated and reliable patterns are selected to produce new pairs from corpus. Various reliability scores are used for new pairs. To measure success of method, 19 target whole words are selected and average 83% (first 10 pairs), 74% (first 20 pairs), 68% (first 30 pairs) precisions are obtained, respectively.
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    Pattern and Semantic Similarity Based Automatic Extraction of Hyponym-Hypernym Relation from Turkish Corpus
    (IEEE, 2015) Sahin, Gurkan; Diri, Banu; Yildiz, Tugba
    Extraction of semantic relations from various resources (Wikipedia, Web, corpus etc.) is an important issue in natural language processing. In this paper, automatic extraction of hyponym-hypernym pairs from Turkish corpus is aimed. For extraction of hyponym-hypernym pairs, pattern and semantic similarity based methods are used together. Patterns are extracted from initial hyponym-hypernym pairs and using patterns, hyponyms are extracted for various hypernyms. Incorrect candidate hyponyms are removed using document frequency and semantic similarity based elimination methods. After experiments for 14 hypernyms, average accuracy of 77% was obtained.