Adwait Ratnaparkhi

A maximum entropy model for prepositional phrase attachment

A parser for natural language must often choose between two or more equally grammatical parses for the same sentence. Often the correct parse can be determined from the lexical properties of certain key words or from the context in which the sentence occurs. For example in the sentence.

Decision tree parsing using a hidden derivation model

Parser development is generally viewed as a primarily linguistic enterprise. A grammarian examines sentences, skillfully extracts the linguistic generalizations evident in the data, and writes grammar rules which cover the language. The grammarian then evaluates the performance of the grammar, and upon analysis of the errors made by the grammar-based parser, carefully refines the rules, repeating this process, typically over a period of several years.

Question answering using maximum entropy components

We present a statistical question answering system developed for TREC-9 in detail. The system is an application of maximum entropy classification for question/answer type prediction and named entity marking. We describe our system for information retrieval which did document retrieval from a local encyclopedia, and then expanded the query words and finally did passage retrieval from the TREC collection. We will also discuss the answer selection algorithm which determines the best sentence given both the question and the occurrence of a phrase belonging to the answer class desired by the question. A new method of analyzing system performance via a transition matrix is shown.