Current Sponsored Research Projects:

AQUAINT AQUINAS Project (Collaboration with Stanford University and ICSI Berkeley)

AQUINAS is an abbreviation for Answering QUestions using INference and Advanced Semantics. The research involves innovations in (1) language analysis, (2) question processing based on complex semantics, (3) indexing using semantic information, (4) extraction and inference of answers, (5) use of corpora and knowledge bases for Question Answering, and (6) learning techniques for abductive reasoning.

Past Sponsored Projects:

AQUAINT Computational Implicatures for Advanced Question Answering (Collaboration with ICSI Berkeley)

The capability of interpreting question implicatures in advanced Question Answering systems is a very important feature. When using a Question Answering system to find information, a professional analyst cannot separate his/her intentions and beliefs from the formulation of the question and therefore (s)he incorporates intentions and beliefs in the interrogation. Moreover, beyond the question, the analyst sometimes makes a proposal or an assertion. This implied information, not recognizable at the syntactic or semantic level, has great importance in the interpretation of a question, and therefore in the quality of the answers returned by a Questions Answering system. This project concerns with the study and development of computational methods that enable coercions of implicatures in the context of advanced Question Answering.

NSF CAREER: Reference Resolution for Natural Language Understanding

A major obstacle in building robust systems that extract and interpret information, and summarize and answer questions from texts, is the need to identify the entities referred to by pronouns or other referential expressions. This project extended work in empirical reference resolution with a learning framework for global optimal decisions. Extentions involved the support of semantic consistency between coreferring expressions and bootstrapping based on large sets of training data.

ARP: Knowledge Mining for Open-Domain Information Extraction

This project created the framework for using complex semantic information in efficient information extraction. By developing state-of-the-art semantic parsers trained on PropBank and FrameNet, knowledge can be mined without extensive domain customization.

NSF CADRE: A Tool for Transforming WordNet into a Core Knowledge Base

This project extended a popular database of English words to make it more useful in such tasks as question answering, information retrieval, and summarization. Wordnet is a lexical database for English that has been widely adopted in artificial intelligence and computational linguistics for a variety of practical applications. The basic elements of WordNet are sets of words that are linked according to semantic relations: synonymy, antonymy, super-ordination, and so forth. WordNet is publicly available, widely used, and is currently being transformed into a multi-lingual database. The focus of the project was to enable the usage of WordNet for knowledge-intensive applications by processing the concept definitions known as glosses. The glosses were part-of-speech tagged, syntactically parsed and semantically disambiguated. To find out more and download the extended WordNet, click here.