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The purpose of PhraseNet is to build a context-sensitive lexical semantic knowledge system, which can help various Natural Language Processing tasks such as question answering and prepositional phrase attachment.
Understanding text to the level that we can extract information from it in an intelligent way and answer questions with respect to it requires the ability to identify different types of entities and categories in text. E.g., this phrase represents a name
A given entity - representing a person, a location, or an organization - may be mentioned in text in multiple, ambiguous ways. Understanding natural language and supporting intelligent access to textual information require identifying whether different mentions of a name, within and across documents, represents the same entity
As the amount of information grows on the web, it becomes harder to find information. Research on Question Answering Systems aims at making the task of finding information easier. The goal is to replace current search technologies, which are based solely
The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they require domain knowledge or supervised ranked data, both of which are expensive to acquire. We investigate learning methods for aggregation of (partial) rankings without supervision.