Varlık ismi tanımlama üzerine kosinüs benzerliği özelliğinin farklı örneklemlerde değerlendirilmesi

Today, social media is a huge part of our world and it continues to grow exponentially. Enormous content is being created with these platforms and it draws the attention of people for personal and professional levels. However, extracting meaningful information from this volume of content with human capabilities is not possible. Machine learning approaches are used to solve this problem with the help of computer power. Natural Language Processing (NLP) is a branch of artificial intelligence which is focused on interacting humans and computers using the natural language. By the aid of machine learning, NLP can achieve tasks from text such as tokenization, classification, sentiment analysis, Named Entity Recognition (NER). These tasks are producing successful results for well-structured texts like newspapers, articles, and books yet, working with unstructured texts from social media are challenging. These types of texts contain emoticons, abbreviations, grammar mistakes, and code-switching making data unpredictable and dirty. Twitter is one of the most popular microblog among social media platforms. It provides texts which are publicly posted and contains topic-specific opinions. It is a valuable source for collecting data. On the other hand, the content is unstructured because of character limitation and casual writing. In this study, we present an NER system and we evaluate baseline classifiers for unstructured texts. We develop cosine similarity feature and we evaluate and test each classifier subject to different combinations of features with cosine similarity. Our experimental results show that the presented system is reached at 74% level in precision, 68% in recall and 67% in F1 (micro average), respectively for Named Entity rEcognition and Linking (NEEL) 2016 Challenge dataset. The corpus is created from Twitter. In addition, we evaluate our system with 2 different datasets with different label distribution and types. One dataset is coming from a startup company named Oxtractor. It has 3 label types; "Person", "Organization", and "Location". Also, we present dataset which is labeled manually from specific topics of tweets. It has 7 types of the label; Person", "Thing", "Organization", "Location", "Product", "Event", and "Character". We compare the prediction results and try to find the best fitting classifiers and feature sets. Logistic regression, SVM, and Random forest are producing the highest results with cosine similarity feature. The results are obtained with different feature sets shows that supportive features for cosine similarly do not impact the results significantly. The diversity of named entity is distinctive when working with cosine similarity feature. Finally, we compare prediction results with different testing/training split ratios for the manually labeled dataset from 90/10 to 50/50. The cosine similarity feature does not affect the split ratio remarkably. Keywords: Named entity recognition, Information Extraction, Twitter, Word embedding, Classification, Machine learning, Cosine Similarity.