| Iustin Dornescu|
(Alternative names for this author)
|Co-authors||Diana Santos, Johannes Leveling, Nuno Cardoso, Orasan C., Paula Carvalho, Sven Hartrumpf, Yvonne Skalban|
|Authorship||Publications (4), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Iustin Dornescu is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Densification: Semantic document analysis using Wikipedia||Natural Language Engineering||English||2014||This paper proposes a new method for semantic document analysis: densification, which identifies and ranks Wikipedia pages relevant to a given document. Although there are similarities with established tasks such as wikification and entity linking, the method does not aim for strict disambiguation of named entity mentions. Instead, densification uses existing links to rank additional articles that are relevant to the document, a form of explicit semantic indexing that enables higher-level semantic retrieval procedures that can be beneficial for a wide range of NLP applications. Because a gold standard for densification evaluation does not exist, a study is carried out to investigate the level of agreement achievable by humans, which questions the feasibility of creating an annotated data set. As a result, a semi-supervised approach is employed to develop a two-stage densification system: filtering unlikely candidate links and then ranking the remaining links. In a first evaluation experiment, Wikipedia articles are used to automatically estimate the performance in terms of recall. Results show that the proposed densification approach outperforms several wikification systems. A second experiment measures the impact of integrating the links predicted by the densification system into a semantic question answering (QA) system that relies on Wikipedia links to answer complex questions. Densification enables the QA system to find twice as many additional answers than when using a state-of-the-art wikification system. Copyright||0||0|
|Semantic QA for encyclopaedic questions: EQUAL in GikiCLEF||Lecture Notes in Computer Science||English||2010||This paper presents a new question answering (QA) approach and a prototype system, EQUAL, which relies on structural information from Wikipedia to answer open-list questions. The system achieved the highest score amongst the participants in the GikiCLEF 2009 task. Unlike the standard textual QA approach, EQUAL does not rely on identifying the answer within a text snippet by using keyword retrieval. Instead, it explores the Wikipedia page graph, extracting and aggregating information from multiple documents and enforcing semantic constraints. The challenges for such an approach and an error analysis are also discussed.||0||0|
|GikiP at GeoCLEF 2008: joining GIR and QA forces for querying Wikipedia||CLEF||English||2009||0||0|
|GikiP at geoCLEF 2008: Joining GIR and QA forces for querying wikipedia||Lecture Notes in Computer Science||English||2009||This paper reports on the GikiP pilot that took place in 2008 in GeoCLEF. This pilot task requires a combination of methods from geographical information retrieval and question answering to answer queries to the Wikipedia. We start by the task description, providing details on topic choice and evaluation measures. Then we offer a brief motivation from several perspectives, and we present results in detail. A comparison of participants' approaches is then presented, and the paper concludes with improvements for the next edition.||0||0|