Adrian Iftene

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Adrian Iftene is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Identifying geographical entities in users' queries Lecture Notes in Computer Science English 2010 In 2009 we built a system in order to compete in the LAGI task (Log Analysis and Geographic Query Identification). The system uses an external resource built into GATE in combination with Wikipedia and Tumba in order to identify geographical entities in user's queries. The results obtained with and without Wikipedia resources are comparable. The main advantage of only using GATE resources is the improved run time. In the process of system evaluation we have identified the main problem of our approach: the system has insufficient external resources for the recognition of geographic entities. 0 0
Methods for classifying videos by subject and detecting narrative peak points Lecture Notes in Computer Science English 2010 2009 marked UAIC's first participation at the VideoCLEF evaluation campaign. Our group built two separate systems for the "Subject Classification" and "Affect Detection" tasks. For the first task we created two resources starting from Wikipedia pages and pages identified with Google and used two tools for classification: Lucene and Weka. For the second task we extracted the audio component from a given video file, using FFmpeg. After that, we computed the average amplitude for each word from the transcript, by applying the Fast Fourier Transform algorithm in order to analyze the sound. A brief description of our systems' components is given in this paper. 0 0
Building a textual entailment system for the RTE3 competition. Application to a QA system Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2008 English 2008 Textual entailment recognition (RTE) is the task of deciding, when given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text. Last year, we built our first Textual Entailment (TE) system, with which we participated in the RTE31 competition. The main idea of this system is to transform the hypothesis making use of extensive semantic knowledge from sources like DIRT, WordNet, Wikipedia and acronyms database. Additionally, the system applies complex grammar rules for rephrasing in English and uses the results of a module we built to acquire the extra background knowledge needed. In the first part, we presented the system architecture and the results, whose best run ranked 3rd in RTE3 among 45 participating runs of 26 groups. The second part of the paper presents the manner in which we adapted the TE system in order to include it in a Question Answering (QA) system. The aim of using the TE system as a module in the general architecture of a QA system is to improve the ranking between possible answers for questions in which the answer type is Measure, Person, Location, Date and Organization. 0 0
Grammar-based automatic extraction of definitions Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2008 English 2008 The paper describes the development and usage of a grammar developed to extract definitions from documents. One of the most important practical usages of the developed grammar is the automatic extraction of definitions from web documents. Three evaluation scenarios were run, the results of these experiments being the main focus of the paper. One scenario uses an e-learning context and previously annotated elearning documents; the second one involves a large collection of unannotated documents (from Wikipedia) and tries to find answers for definition type questions. The third scenario performs a similar question-answering task, but this time on the entire web using Google web search and the Google Translation Service. The results are convincing, further development as well as further integration of the definition extraction system in various related applications are already under way. 0 0
Named Entity Relation Mining using Wikipedia English 2008 0 0