IN THIS PAPER, WE DESCRIBE OUR PROPOSED METHODOLOGY FOR CONSTRUCTING AN ONTOLOGY OF Natural Language Processing (NLP). WE USE A SEMI-AUTOMATIC METHOD; A COMBINATION OF RULE-BASED AND MACHINE LEARNING TECHNIQUES; TO CONSTRUCT AND POPULATE AN ONTOLOGY WITH BILINGUAL (ENGLISH-PERSIAN) CONCEPT LABELS (LEXICON) AND EVALUATE IT MANUALLY. THIS METHODOLOGY RESULTS IN A COMPLETE ONTOLOGY IN THE Natural Language Processing DOMAIN WITH 887 CONCEPTS, 88 RELATIONS, AND 71 FEATURES. THE BUILT ONTOLOGY IS POPULATED WITH NEAR 36000 NLP RELATED PAPERS AND 32000 AUTHORS, AND ABOUT 201000 "IS_RELATED_TO", 83500 "IS_AUTHOR_OF", AND 29000 "PRESENTED_IN" RELATIONS. THE INSTANTIATION IS DONE TO ENABLE APPLICATIONS FIND EXPERTS, PUBLICATIONS AND INSTITUTIONS RELATED TO VARIOUS TOPICS IN NLP FIELD.

THE PURPOSE OF THIS PAPER IS TO STUDY THE TASKS OF Natural Language Processing (NLP) IN MUSIC INFORMATION RETRIEVAL. MUSIC COMPUTATIONAL RESEARCH IN THE FIELD SUGGESTS THAT THE AREA OF NLP IS CLOSE TO RECENT RESEARCH IN MUSIC. SO WE CAN PROFIT FROM THIS SIMILARITY SEARCH NLP TECHNIQUES APPLIED IN MUSIC. IN THIS PAPER, THE SIMILARITY BETWEEN BOTH THE ABOVE DESCRIBED AREA. NLP LEVELS LISTED HERE REFER TO RELEVANT TASKS IN COMPUTATIONAL STUDY MUSIC. NLP BRIEF INTRODUCTION ABOUT THE HISTORY OF MUSIC IN THE BACKGROUND MAKES CLEAR THE FOLLOWING DIFFERENT ASPECTS OF THE RESEARCH ABOUT THE MUSIC, GIVEN ITS PROXIMITY TO THE NLP, IS INTRODUCED. FINALLY, RECENT RESEARCH ON MUSIC COMPUTATIONAL RESEARCH COMPARED WITH SIMILAR IDEAS IN NLP AND PRESENTED GUIDELINES FOR MUSIC INFORMATION RETRIEVAL.

In this article, we introduce solutions for solving crossword puzzles by machine using Natural Language Processing techniques. This task is divided into two subtasks of finding possible answers for each table description and then selecting the target word and placing it in the table. The first subtask, which is dedicated to finding the word from its description, has many other uses as in text generation and paraphrasing. For this purpose, we used a combination of different methods, including searching and finding semantic similarities on the data of previously solved tables, searching in dictionary and Wikipedia articles, using a masked Language model, and finding related words in Farsnet and the Farsiyar tool. The results show that the combination of these methods has a better result (82% recall) compared to their individual implementation. In the next subtask, we give the list of possible answers to a constraint-satisfaction search algorithm to choose the correct answer that can be placed in the table, taking into account the constraints of the table, and fill the empty cells in the best way and solve the crossword. The overall evaluation shows 80. 22% precision and 68. 86% recall in solving the crossword puzzle.