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Abstract This report describes our approach to the Robust-Word Sense Disambiguation task. We applied the same index expansion technique used in 2008 for the Question Answering WSD task, with the addition of pseudo (blind) relevance feedback. In our approach, a WordNet expanded index is generated from the disambiguated document collection. This index contains synonyms, hypernyms and holonyms of the disambiguated words contained in documents.
2010
This paper describes a method developed for the Robust-Word Sense Disambiguation task at CLEF 2009. In our approach, a WordNet expanded index is generated from the disambiguated document collection. This index contains synonyms, hypernyms and holonyms of the disambiguated words contained in documents. Query words are integrated by terms extracted by means of a pseudo relevance feedback technique.
Lecture Notes in Computer Science, 2009
2010
Abstract In recent years, geography has acquired a great importance in the context of Information Retrieval (IR) and, in general, of the automated processing of information in text. Mobile devices that are able to surf the web and at the same time inform about their position are now a common reality, together with applications that can exploit this data to provide users with locally customised information, such as directions or advertisements.
Working Notes for the CLEF 2008 Workshop, 2017
This report describes our approach to the Question Answering - Word Sense Disam-biguation task. In our approach, disambiguated documents are used to improve the retrieval phase: this has been implemented by adding a WordNet expanded index to the ...
2007
Abstract One of the first scenarios imagined by the researchers in Artificial Intelligence was the problem of conversing with a machine in natural language. Alan Turing in 1950 proposed a test in order to check the capability of a machine to demonstrate intelligence, and that test, that carries his name, is mostly based on conversation and language understanding. Obtaining responses to questions has always been the ambition of the human being.
2008
Abstract This year our system was complemented with a map-based filter. During the indexing phase, all places are disambiguated and assigned their coordinates on the map. These coordinates are stored in a separate index. The search process is carried out in two phases: in the first one, we search the collection with the same method applied in 2007, which exploits the expansion of index terms by means of WordNet synonyms and holonyms.
My thesis aims to augment the Geographic Information Retrieval process with information extracted from world knowledge. This aim is approached from three directions: classifying world knowledge, disambiguating placenames and modelling users. Geographic information is becoming ubiquitous across the Internet, with a significant proportion of web documents and web searches containing geographic entities, and the proliferation of Internet enabled mobile devices. Traditional information retrieval treats these geographic entities in the same way as any other textual data. In this thesis I augment the retrieval process with geographic information, and show how methods built upon world knowledge outperform methods based on heuristic rules. The source of world knowledge used is Wikipedia. Wikipedia has become a phenomenon of the Internet age and needs little introduction. As a linked corpus of semi-structured data, it is unsurpassed. Two approaches to mining information from Wikipedia are rigorously explored: initially I classify Wikipedia articles into broad categories; this is followed by much finer classification where Wikipedia articles are disambiguated as specific locations. The thesis concludes with the proposal of the Steinberg hypothesis: By analysing a range of wikipedias in different languages I demonstrate that a localised view of the world is ubiquitous and inherently part of human nature. All people perceive closer places as larger and more important than distant ones. The core contributions of mythesis are in the areas of extracting information from Wikipedia, supervised placename disambiguation, and providing a quantitative model for how people view the world. The findings clearly have a direct impact for applications such as geographically aware search engines, but in a broader context documents can be automatically annotated with machine readable meta-data and dialogue enhanced with a model of how people view the world. This will reduce ambiguity and confusion in dialogue between people or computers.
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