Adlandırılmış varlık ile ad aktarması çözümleme

Internet and computers has become more and more important in human life. Especiallywith Web 2.0, the permission given to surfers of not only reading but also changingthe content has encouraged humans to change their habits of explorations. Nowadaysmankind use internet instead of encyclopaedias and books. This undeniable growth ofdigitalism put forth the importance of automatically processing of the data. Automati-cally processing the content means turning unstructured data into structured data. Byunstructured data we connote human language – natural language data. This treat-ment is necessary for computers to understand the natural language content. NaturalLanguage Processing (NLP) is the discipline behind this process. NLP is a subcategoryof artificial intelligence and computer linguistics.Named Entity Recognition (NER) and Word Sense Disambiguation (WSD) is two ofthe major tasks of NLP. NER is the classification and extraction process of word(s)considered significant in a text. This significant word(s) can differ according to field.For example, this entities may be percentages, dates as well as person names, locationnames and company names, etc. WSD is an open problem in NLP. It consists ofidentifying the sense of a word, when having multiple meaning, in a sentence. WSDtries to identify litteral expressions of a word, not figurative expressions. FigurativeLanguage Processing is the study similar and all but subfield of WSD. FigurativeLanguage Processing concentrates on determining figurative expressions except litteralexpressions.Our project is based on metonymy recognition and resolution through named entityrecognition. Metonymy is a figure of speech which consists by using a concept b to referto concept a, without intending analogy. The existing methods of metonymy resolutiondepends on supervised and unsupervised methods as well as statistical approaches. Thecommonly used approaches are catching the Selectional Restriction Violations (SRVs)and deviations from grammatical rules. We consider our project having three parts.First part is to pre-process the given text. Pre-processing is necessary for further treatment. Pre-processing consists of lemma-tization, part-of-speech tagging, NER tagging, dependency tagging and WSD treat-ment. The second part is metonymy recognition, in other words detections of possiblemetonymies. Metonymy recognition is realized via named entities' SRVs. It is doneby a rule based algorithm. The last and the third part is metonymy resolution whichconsists of determining metonymic relation.