Browsing by Subject "Word processing"
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Item Open Access Haber videolarında nesne tanıma ve otomatik etiketleme(IEEE, 2006-04) Baştan, Muhammet; Duygulu, PınarWe propose a new approach to object recognition problem motivated by the availability of large annotated image and video collections. Similar to translation from one language to another, this approach considers the object recognition problem as the translation of visual elements to words. The visual elements represented in feature space are first categorized into a finite set of blobs. Then, the correspondences between the blobs and the words are learned using a method adapted from Statistical Machine Translation. Finally, the correspondences, in the form of a probability table, are used to predict words for particular image regions (region naming), for entire images (auto-annotation), or to associate the automatically generated speech transcript text with the correct video frames (video alignment). Experimental results are presented on TRECVID 2004 data set, which consists of about 150 hours of news videos associated with manual annotations and speech transcript text. © 2006 IEEE.Item Open Access Language evolution and information theory(IEEE, 2004-06-07) Ahlswede, R.; Arıkan, Erdal; Bäumer, L.; Deppe, C.The use of Nowak's model to study the language evolution and to settle a conjecture by Nowak was discussed. These models explained the ways by which natural selection can lead to the gradual emergence of human language. It was shown that the Nowak's conjecture is true for a class of spaces defined by a certain condition on the distance function. A connection between Nowak's model and standard information-theoretic models was indicated.Item Open Access Lexical cohesion based topic modeling for summarization(Springer, 2008-02) Ercan, Gönenç; Çiçekli, İlyasIn this paper, we attack the problem of forming extracts for text summarization. Forming extracts involves selecting the most representative and significant sentences from the text. Our method takes advantage of the lexical cohesion structure in the text in order to evaluate significance of sentences. Lexical chains have been used in summarization research to analyze the lexical cohesion structure and represent topics in a text. Our algorithm represents topics by sets of co-located lexical chains to take advantage of more lexical cohesion clues. Our algorithm segments the text with respect to each topic and finds the most important topic segments. Our summarization algorithm has achieved better results, compared to some other lexical chain based algorithms. © 2008 Springer-Verlag Berlin Heidelberg.Item Open Access On document relevance and lexical cohesion between query terms(Elsevier, 2006) Vechtomova, O.; Karamuftuoglu, M.; Robertson, S. E.Lexical cohesion is a property of text, achieved through lexical-semantic relations between words in text. Most information retrieval systems make use of lexical relations in text only to a limited extent. In this paper we empirically investigate whether the degree of lexical cohesion between the contexts of query terms' occurrences in a document is related to its relevance to the query. Lexical cohesion between distinct query terms in a document is estimated on the basis of the lexical-semantic relations (repetition, synonymy, hyponymy and sibling) that exist between there collocates - words that co-occur with them in the same windows of text. Experiments suggest significant differences between the lexical cohesion in relevant and non-relevant document sets exist. A document ranking method based on lexical cohesion shows some performance improvements. © 2006 Elsevier Ltd. All rights reserved.Item Open Access Query expansion with terms selected using lexical cohesion analysis of documents(Elsevier Ltd, 2007-07) Vechtomova, O.; Karamuftuoglu, M.We present new methods of query expansion using terms that form lexical cohesive links between the contexts of distinct query terms in documents (i.e., words surrounding the query terms in text). The link-forming terms (link-terms) and short snippets of text surrounding them are evaluated in both interactive and automatic query expansion (QE). We explore the effectiveness of snippets in providing context in interactive query expansion, compare query expansion from snippets vs. whole documents, and query expansion following snippet selection vs. full document relevance judgements. The evaluation, conducted on the HARD track data of TREC 2005, suggests that there are considerable advantages in using link-terms and their surrounding short text snippets in QE compared to terms selected from full-texts of documents. © 2006 Elsevier Ltd. All rights reserved.Item Open Access Retrieval of Ottoman documents(ACM, 2006-10) Ataer, Esra; Duygulu, PınarThere is a growing need to access historical Ottoman documents stored in large archives and therefore managing tools for automatic searching, indexing and transcription of these documents is required. In this paper, we present a method for the retrieval of Ottoman documents based on word matching. The method first successfully segments the documents into word images and then uses a hierarchical matching technique to find the similar instances of the word images. The experiments show that even with simple features promising results can be achieved. Copyright 2006 ACM.