NATURAL LANGUAGE PROCESSING ALGORITHMS AND PERFORMANCE COMPARISON

Year 2024, Volume: 9 Issue: 2, 106 - 121, 30.10.2024

Ayhan Arısoy

https://doi.org/10.57120/yalvac.1536202

Abstract

Natural language processing (NLP) is the general name for the methods and algorithms developed for computers to understand, interpret and produce human language. NLP plays a critical role in many fields, from social media analyses to customer service, from language translation to healthcare. This paper provides a comprehensive overview of the basic concepts of NLP, popular algorithms and models, performance comparisons, and various application areas. Key concepts of NLP include language models, tokenisation, lemmatisation, stemming, POS tagging, NER and syntactic parsing. These concepts are critical for processing, analysing and making sense of texts. Language models include popular methods such as N-gram, Word2Vec, GloVe and BERT. NLP algorithms are classified as rule-based methods, machine learning methods and deep learning methods. Rule-based methods are based on grammatical rules, while machine learning methods work on the principle of learning from data. Deep learning methods, on the other hand, achieve high accuracy results by using large datasets and powerful computational resources. In the performance comparison section, it is stated that the algorithms are evaluated with metrics such as accuracy, precision, recall and F1 score. Advanced models such as BERT and GPT-3 show superior performance in many NLP tasks. In conclusion, the field of NLP is rapidly evolving, with significant advancements anticipated in several key areas. These include the creation of more effective and efficient models, efforts to reduce biases, enhanced privacy protection, the growth of multilingual and cross-cultural models, and the development of explainable artificial intelligence techniques. This paper provides a comprehensive overview to understand the current status and future directions of NLP technologies.

Keywords

NLP, Language Models, Deep Learning, Text Analysis

DOĞAL DİL İŞLEME ALGORİTMALARI VE PERFORMANS KARŞILAŞTIRMASI

Abstract

Doğal dil işleme (NLP), bilgisayarların insan dilini anlaması, yorumlaması ve üretmesi için geliştirilen yöntem ve algoritmaların genel adıdır. NLP, sosyal medya analizlerinden müşteri hizmetlerine, dil çevirisinden sağlık hizmetlerine kadar birçok alanda kritik bir rol oynamaktadır. Bu makale, NLP'nin temel kavramları, popüler algoritmalar ve modeller, performans karşılaştırmaları ve çeşitli uygulama alanları hakkında kapsamlı bir genel bakış sunmaktadır. NLP'nin temel kavramları arasında dil modelleri, tokenisation, lemmatisation, stemming, POS tagging, NER ve syntactic parsing yer almaktadır. Bu kavramlar metinlerin işlenmesi, analiz edilmesi ve anlamlandırılması için kritik öneme sahiptir. Dil modelleri N-gram, Word2Vec, GloVe ve BERT gibi popüler yöntemleri içerir. NLP algoritmaları kural tabanlı yöntemler, makine öğrenimi yöntemleri ve derin öğrenme yöntemleri olarak sınıflandırılır. Kural tabanlı yöntemler dilbilgisi kurallarına dayanırken, makine öğrenimi yöntemleri veriden öğrenme prensibiyle çalışır. Derin öğrenme yöntemleri ise büyük veri kümeleri ve güçlü hesaplama kaynakları kullanarak yüksek doğrulukta sonuçlar elde etmektedir. Performans karşılaştırma bölümünde algoritmaların doğruluk, kesinlik, geri çağırma ve F1 skoru gibi metriklerle değerlendirildiği belirtilmektedir. BERT ve GPT-3 gibi gelişmiş modeller birçok NLP görevinde üstün performans göstermektedir. Sonuç olarak, NLP alanı hızla gelişmekte ve birkaç kilit alanda önemli ilerlemeler beklenmektedir. Bunlar arasında daha etkili ve verimli modellerin oluşturulması, önyargıları azaltma çabaları, gelişmiş gizlilik koruması, çok dilli ve kültürler arası modellerin büyümesi ve açıklanabilir yapay zeka tekniklerinin geliştirilmesi yer almaktadır. Bu makale, NLP teknolojilerinin mevcut durumunu ve gelecekteki yönelimlerini anlamak için kapsamlı bir genel bakış sunmaktadır.

Keywords

NLP, Language Models, Deep Learning, Text Analysis

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