Browsing by Subject "Information Extraction"
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Item Open Access Automating information extraction task for Turkish texts(2011) Tatar, SerhanThroughout history, mankind has often suffered from a lack of necessary resources. In today’s information world, the challenge can sometimes be a wealth of resources. That is to say, an excessive amount of information implies the need to find and extract necessary information. Information extraction can be defined as the identification of selected types of entities, relations, facts or events in a set of unstructured text documents in a natural language. The goal of our research is to build a system that automatically locates and extracts information from Turkish unstructured texts. Our study focuses on two basic Information Extraction (IE) tasks: Named Entity Recognition and Entity Relation Detection. Named Entity Recognition, finding named entities (persons, locations, organizations, etc.) located in unstructured texts, is one of the most fundamental IE tasks. Entity Relation Detection task tries to identify relationships between entities mentioned in text documents. Using supervised learning strategy, the developed systems start with a set of examples collected from a training dataset and generate the extraction rules from the given examples by using a carefully designed coverage algorithm. Moreover, several rule filtering and rule refinement techniques are utilized to maximize generalization and accuracy at the same time. In order to obtain accurate generalization, we use several syntactic and semantic features of the text, including: orthographical, contextual, lexical and morphological features. In particular, morphological features of the text are effectively used in this study to increase the extraction performance for Turkish, an agglutinative language. Since the system does not rely on handcrafted rules/patterns, it does not heavily suffer from domain adaptability problem. The results of the conducted experiments show that (1) the developed systems are successfully applicable to the Named Entity Recognition and Entity Relation Detection tasks, and (2) exploiting morphological features can significantly improve the performance of information extraction from Turkish, an agglutinative language.Item Open Access A statistical information extraction system for Turkish(2000) Tür, GökhanThis thesis presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods. We have successfully applied statistical methods using both the lexical and morphological information to the following tasks: -The Turkish Text Deasciifier task aims to convert the ASCII characters in a Turkish text, into the corresponding non-ASCII Turkish characters (i.e.,"ü", "ö", "ç", "ş", "ğ", "ı", and their upper cases). -The Word Segmentation task aims to detect word boundaries, given we have a sequence of characters without space or punctuation.-The Vowel Restoration task aims to restore the vowels of an input stream, whose vowels are deleted.-The Sentence Segmentation task aims to divide a stream of text or speech into grammatical sentences. Given a sequence of (written or spoken) words, the aim of sentence segmentation is to find the boundaries of the sentences.-The Topic Segmentation task aims to divide a stream of text or speech into topically homogeneous blocks. Given a sequence of (written or spoken) words, the aim of topic segmentation is to find the boundaries where topics change.-The Name Tagging task aims to mark the games (persons, locations, and organizations) in a text. For relatively simpler tasks, such as Turkish Text Deasciifier, Word Segmentation, and Vowel Restoration, only lexical information is enough, but in order to obtain better performance in more complex tasks, such as Sentence Segmentation, Topic Segmentation, and Name Tagging, we not only use lexical information, but also exploit morphological and contextual information. For sentence segmentation, we have modeled the final inflectional groups of the words and combined them with the lexical model, and decreased the error rate to 4.34%. For name tagging, in addition to the lexical and morphological models, we have also employed contextual and tag models, and reached an F-measure of 91.56%. For topic segmentation, stems of the words (nouns) have been found to be more effective than using the surface forms of the words and we have achieved 10.90% segmentation error rate on our test set.