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rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other

Sentence segmentation and punctuation recovery are critical components for effective spoken language translation (SLT). In this paper we describe our recent work on sentence segmentation and punctuation recovery for three different language pairs, namely for English-to-Spanish, Arabic

The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually formatted for use by people (e.g., the relevant content is embedded in HTML pages), so extracting their content is difficult. Wrappers are often used for this purpose. A wrapper is a procedure for extracting a particular resource's content. Unfortunately, hand-coding wrappers is tedious. We introduce wrapper induction, a technique for automatically constructing wrappers. Our techniques can be described in terms of three main contributions. First, we pose the problem of wrapper construction as one of inductive learn...

Abstract not found

In this paper, we describe improved alignment models for statistical machine translation. The statistical translation approach uses two types of information: a translation model and a lan- guage model. The language model used is a bigram or general m-gram model. The translation model

This study reports the results of using minimum description length (MDL) analysis to model unsupervised learning of the morphological segmentation of European languages, using corpora ranging in size from 5,000 words to 500,000 words. We develop a set of heuristics that rapidly develop a

We describe an implemented system which automatically generates and animates conversations between multiple human-like agents with appropriate and synchronized speech, intonation, facial expressions, and hand gestures. Conversations are created by a dialogue planner that produces the text as well as the intonation of the utterances. The speaker/listener relationship, the text, and the intonation in turn drive facial expressions, lip motions, eye gaze, head motion, and arm gesture generators. Coordinated arm, wrist, and hand motions are invoked to create semantically meaningful gestures. Throughout, we will use examples from an actual synthesized, fully animated conversation.

This paper studies the impact of automatic sentence segmentation and punctuation prediction on the quality of machine translation of automatically recognized speech. We present a novel sentence segmentation method which is specifically tailored to the requirements of machine translation algorithms

We describe an implemented system which automatically generates and animates conversations between multiple human-like agents with appropriate and synchronized speech, intonation, facial expressions, and hand gestures. Conversations are created by a dialogue planner that produces the text as well as the intonation of the utterances. The speaker/listener relationship, the text, and the intonation in turn drive facial expressions, lip motions, eye gaze, head motion, and arm gesture generators. Coordinated arm, wrist, and hand motions are invoked to create semantically meaningful gestures. Throughout, we will use examples from an actual synthesized, fully animated conversation. 1