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)
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)
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)
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