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Yayın A new approach for named entity recognition(IEEE, 2017) Ertopçu, Burak; Kanburoğlu, Ali Buğra; Topsakal, Ozan; Açıkgöz, Onur; Gürkan, Ali Tunca; Özenç, Berke; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerMany sentences create certain impressions on people. These impressions help the reader to have an insight about the sentence via some entities. In NLP, this process corresponds to Named Entity Recognition (NER). NLP algorithms can trace a lot of entities in the sentence like person, location, date, time or money. One of the major problems in these operations are confusions about whether the word denotes the name of a person, a location or an organisation, or whether an integer stands for a date, time or money. In this study, we design a new model for NER algorithms. We train this model in our predefined dataset and compare the results with other models. In the end we get considerable outcomes in a dataset containing 1400 sentences.Yayın Word sense disambiguation, named entity recognition, and shallow parsing tasks for Turkish(Işık Üniversitesi, 2019-04-02) Topsakal, Ozan; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıPeople interactions are based on sentences. The process of understanding sentences is thru converging, parsing the words and making sense of words. The ultimate goal of Natural Language Processing is to understand the meaning of sentences. There are three main areas that are the topics of this thesis, namely, Named Entity Recognition, Shallow Parsing, and Word Sense Disambiguation. The Natural Language Processing algorithms that learn entities, like person, location, time etc. are called Named Entity Recognition algorithms. Parsing sentences is one of the biggest challenges in Natural Language Processing. Since time efficiency and accuracy are inversely proportional with each other, one of the best ideas is to use shallow parsing algorithms to deal with this challenge. Many of words have more than one meaning. Recognizing the correct meaning that is used in a sentence is a difficult problem. In Word Sense Disambiguation literature there are lots of algorithms that can help to solve this problem. This thesis tries to find solutions to these three challenges by applying machine learning trained algorithms. Experiments are done on a dataset, containing 9,557 sentences.Yayın Entity-relationship diagram generation with natural language processing and machine learning approach(Işık Üniversitesi, 2023-08-24) Köprülü, Mertali; Ekin, Emine; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıAs software systems continue to grow in complexity, the need for efficient and accurate design methodologies becomes increasingly critical. Entity-Relationship Diagrams (ERDs) provide a powerful visual representation of system structures and dependencies, serving as a foundation for software engineering and database design. However, manually creating ERDs from textual requirements is time-consuming and manual. To address this challenge, this research explores the application of natural language processing (NLP) techniques to automatically extract relevant information from unstructured text and generate ERDs. The proposed approach leverages the strengths of rule-based techniques, semantic analysis, and machine learning algorithms to automatically identify entities, attributes, relationships, and cardinalities from natural language input. Our study offers practical insights into the utilization of linguistic and semantic analysis, and machine learning for efficient information extraction. The proposed system aims to streamline the ERD creation process and improve the accuracy and quality of the resulting diagrams. While the proposed approach shows promising results, the limitations in heuristic rule coverage and data dependencies are acknowledge. Furthermore, the evaluation results demonstrate in detecting entities, attributes, and relations, with f1-scores of 0.96, 0.93, and 0.92, and resolving the components specifications achieved accuracy of 0.87, 0.84, 0.91, respectively. The findings contribute to advancing ERD extraction from text and suggest future research directions for improving the robustness and usability of the solution. The fusion of NLP techniques with ERD creation highlights the potential for enhancing the software development lifecycle and opens new avenues for research in the realm of information extraction from natural language text.
