Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Building a Generation Knowledge Source using Internet-Accessible Newswire

In this paper, we describe a method for automatic creation of a knowledge source for text generation using information extraction over the Internet. We present a prototype system called PROFILE which uses a client-server architecture to extract noun-phrase descriptions of entities such as people, places, and organizations. The system serves two purposes: as an information extraction tool, it allows users to search for textual descriptions of entities; as a utility to generate functional descriptions (FD), it is used in a functional-unification based generation system. We present an evaluation of the approach and its applications to natural language generation and summarization.Comment: 8 pages, uses eps

Gathering Statistics to Aspectually Classify Sentences with a Genetic Algorithm

This paper presents a method for large corpus analysis to semantically classify an entire clause. In particular, we use cooccurrence statistics among similar clauses to determine the aspectual class of an input clause. The process examines linguistic features of clauses that are relevant to aspectual classification. A genetic algorithm determines what combinations of linguistic features to use for this task.Comment: postscript, 9 pages, Proceedings of the Second International Conference on New Methods in Language Processing, Oflazer and Somers ed

Focus Constraints on Language Generation

Computer generation of natural language requires the ability to make reasoned choices from a large number of possible things to say as well as from a large number of expressive possibilties. This paper examines in detail how one influence on a generated text, focus of attention, can be used to constrain the many possibilities that a generation system must consider. A computational treatment of focus of attention is presented that can be used to constrain what the system needs to consider when deciding what to say next. In this process, information is produced that provides constraints on which words and syntactic structures best express the system's intent, thus ensuring that its resulting text is coherent. This analysis has been used in the fully implemented TEXT system which generates paragraph length responses to questions about database structure

Language Generation: Applications, Issues and Approaches

As computer systems become more sophisticated they must be able to communicate their results successfully to their users. Natural language generation is the area of research concerned with developing methods that will allow a computer system to respond to its user in human language. In this paper, the need for natural language generation is first motivated by showing how it is used in several applications. Given that language generation is necessary for such systems, the paper also focuses on the issues that must be taken into account in developing a system that can generate language. Finally, techniques that have been used in two question-answering systems, the TEXT system [21] and TAILOR [22], are discussed

The need for text generation

For a variety of systems, such as expert systems, database systems, and problem-solving systems, text generation is one way for the system to communicate effectively with its users. This is particularly true when the system is likely to be used by a wide range of users with varying levels of expertise and background. In this paper I will show why explanation is a crucial feature of expert systems, how text generation can be used within database systems to familiarize users with the database, and where text generation can aid communication with problem-solving systems. Given that text generation is more than just a frill for such systems, a second focus of the paper will be on the kinds of problems that any designer of a text generation system must address. Some of the problems include being able to decide what to say, how to organize that information, and how to express it in natural language