Marian Olteanu

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This paper reports on Language Computer Corporation's first QA@CLEF participation. For this exercise, we integrated our open-domain PowerAnswer question answering system with our statistical machine translation engine. For 2006, we participated in the English-to-Spanish, French and Portuguese cross-language tasks. We took the approach of intermediate(More)
This paper reports on Language Computer Corporation's QA@CLEF 2007 preparation, participation and results. For this exercise , LCC integrated its open-domain PowerAnswer Question Answering system with its statistical Machine Translation engine. For 2007, LCC participated in the English-to-French and English-to-Portuguese cross-language tasks. The approach(More)
The discovery of semantic relations in text plays an important role in many NLP applications. This paper presents a method for the automatic classification of semantic relations in nominalized noun phrases. Nominalizations represent a subclass of NP constructions in which either the head or the modifier noun is derived from a verb while the other noun is an(More)
Analysts in various domains, especially intelligence and financial, have to constantly extract useful knowledge from large amounts of unstructured or semi-structured data. Keyword-based search, faceted search, question-answering, etc. are some of the automated methodologies that have been used to help analysts in their tasks. General-purpose and(More)
Complex Language Models cannot be easily integrated in the first pass decoding of a Statistical Machine Translation system – the decoder queries the LM a very large number of times; the search process in the decoding builds the hypotheses incremen-tally and cannot make use of LMs that analyze the whole sentence. We present in this paper the Language(More)
We consider the problem of resolving duplicates in a database of places, where a place is defined as any entity that has a name and a physical location. When other auxiliary attributes like phone and full address are not available, deduplication based solely on names and approximate location becomes an exceptionally challenging problem that requires both(More)
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