Kelime Birliktelik Madenciliği Tekniklerinin İncelenmesi

Bu çalışma, koşullu entropi, ortak bilgi (MI) değerleri, log-birliktelik oranı (LLR) ve basit ortak oluşumların güçlü sözdizimsel ilişkilerin çıkarılması üzerindeki etkisinin araştırılmasını sunmaktadır. Deneyler, 10.000 restoran yorumunu içeren Yelp Akademik Veri Kümesi kullanılarak gerçekleştirilmiştir. Ortak bilgi değeri en yüksek sözcük çiftlerinin, söz dizimsel olarak ilişkili en üstteki sözcükleri derlemden çıkardığı kabul edilir. Bu amaçla Spyder 3.3.6 ve Python Natural Language Toolkit (NLTK) Library kullanılmıştır. Ortak bilgi değerleri daha sonra basit ortak oluşum sayısı ile karşılaştırılır. Analiz sonuçları, üç farklı kelime eşdizimleme tekniğinin benzer sonuçlar verdiğini ve bu nedenle, bunların hepsinin Kelime eşdizimleri için etkili bir şekilde kullanılabileceğini göstermiştir.

Anahtar Kelimeler:

Kelime birlikteliği, Birliktelik madenciliği, Eşdizim çıkarma, Ortak bilgi, Metin madenciliği

Investigating Word Association Mining Techniques

This study presents the investigation of the effect of conditional entropy, mutual information (MI) values, log-likelihood ratio (LLR), and simple co-occurrences on extracting strong syntagmatic relationships. Experiments are conducted by using the Yelp Academic Dataset, which includes extracted 10.000 restaurant reviews. The mutual information values of word pairs are considered to extract the top syntagmatically related words from the corpus. For this purpose, Spyder 3.3.6 and Python Natural Language Toolkit (NLTK) Library are used. The mutual information values are then compared with simple co-occurrences count. The analysis results indicated that the three Word collocation techniques give similar results and therefore, all of those can be employed for Word collocations effectively.

Keywords:

Word Collocation, Collocation Mining, Collocation extraction, Mutual Information, Text Mining,

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