This paper describes an application of reinforcement learning to determine a dialog policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. With this approach a useful ...

This paper addresses a dialogue strategy to clarify and constrain the queries for speech-driven document retrieval systems. In spoken dialogue interfaces, users often make utterances before the query is completely generated in their mind; thus input ...

Many language processing tasks can be reduced to breaking the text into segments with prescribed properties. Such tasks include sentence splitting, tokenization, named-entity extraction, and chunking. We present a new model of text segmentation based on ...

This work addresses the task of identifying thematic correspondences across sub-corpora focused on different topics. We introduce an unsupervised algorithmic framework based on distributional data clustering, which generalizes previous initial works on ...

This paper explores the segmentation of tutorial dialogue into cohesive topics. A latent semantic space was created using conversations from human to human tutoring transcripts, allowing cohesion between utterances to be measured using vector ...

Applying the noisy channel model to search query spelling correction requires an error model and a language model. Typically, the error model relies on a weighted string edit distance measure. The weights can be learned from pairs of misspelled words ...

MIT's Audio Notebook added great value to the note-taking process by retaining audio recordings, e.g. during lectures or interviews. The key was to provide users ways to quickly and easily access portions of interest in a recording. Several non-speech-...

In this paper we investigate the use of linguistic knowledge in passage retrieval as part of an open-domain question answering system. We use annotation produced by a deep syntactic dependency parser for Dutch, Alpino, to extract various kinds of ...

We investigate techniques to support the answering of opinion-based questions. We first present the OpQA corpus of opinion questions and answers. Using the corpus, we compare and contrast the properties of fact and opinion questions and answers. Based ...

We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. Annotators generated a list of questions central to understanding each story in our corpus. Because of the ...

In this paper we investigate an application of feature clustering for word sense disambiguation, and propose a semisupervised feature clustering algorithm. Compared with other feature clustering methods (ex. supervised feature clustering), it can infer ...

Measuring the relative compositionality of Multi-word Expressions (MWEs) is crucial to Natural Language Processing. Various collocation based measures have been proposed to compute the relative compositionality of MWEs. In this paper, we define novel ...

This paper addresses the automatic classification of the semantic relations expressed by the English genitives. A learning model is introduced based on the statistical analysis of the distribution of genitives' semantic relations on a large corpus. The ...

We develop an unsupervised semantic role labelling system that relies on the direct application of information in a predicate lexicon combined with a simple probability model. We demonstrate the usefulness of predicate lexicons for role labelling, as ...

Dictionaries and word translation models are used by a variety of systems, especially in machine translation. We build a multilingual dictionary induction system for a family of related resource-poor languages. We assume only the presence of a single ...

We present a lexicon-free post-processing method for optical character recognition (OCR), implemented using weighted finite state machines. We evaluate the technique in a number of scenarios relevant for natural language processing, including creation ...

This paper considers the problem of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We introduce a general framework for semantic projection which exploits parallel texts, is relatively inexpensive and can ...

The lack of annotated data is an obstacle to the development of many natural language processing applications; the problem is especially severe when the data is non-English. Previous studies suggested the possibility of acquiring resources for non-...

We here propose a new method which sets apart domain-specific terminology from common non-specific noun phrases. It is based on the observation that terminological multi-word groups reveal a considerably lesser degree of distributional variation than ...

Recent work has shown that very large corpora can act as training data for NLP algorithms even without explicit labels. In this paper we show how the use of surface features and paraphrases in queries against search engines can be used to infer labels ...