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 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.
Named Entity recognition (NER) is an important part of many natural language processing tasks. Current approaches often employ machine learning techniques and require supervised data, which are lacking in many domains. We investigate methods to automatically acquire such resources.
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 me
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
