that the translator has already produced. (b) TransTalk is
an automatic dictation system that makes use of a probabilistic
translation model in order to improve the performance of its voice
recognition model. (c) TransCheck automatically detects certain types
of translation errors by verifying that the correspondences between the
segments of a draft and the segments of the source text respect well-
known properties of a good translation. (d) TransSearch allows
translators to search databases of pre-existing translations in order
to find ready-made solutions to all sorts of translation problems. In
order to produce the required databases, the translations and the
source language texts must first be aligned."

# Natural Language Group

The Natural Language Group (NLG) at the Information Sciences Institute
(ISI) of the University of Southern California (USC) has been involved
in various aspects of computational/natural language processing:
machine translation, automated text summarization, multilingual verb
access and text management, development of large concept taxonomies
(ontologies), discourse and text generation, construction of large
lexicons for various languages, and multimedia communication.

Eduard Hovy, head of the Natural Language Group, explained in August
1998: "People will write their own language for several reasons --
convenience, secrecy, and local applicability -- but that does not mean
that other people are not interested in reading what they have to say!
This is especially true for companies involved in technology watch
(say, a computer company that wants to know, daily, all the Japanese
newspaper and other articles that pertain to what they make) or some
Government Intelligence agencies (the people who provide the most up-
to-date information for use by your government officials in making
policy, etc.). One of the main problems faced by these kinds of people
is the flood of information, so they tend to hire 'weak' bilinguals who
can rapidly scan incoming text and throw out what is not relevant,
giving the relevant stuff to professional translators. Obviously, a
combination of SUM (automated text summarization) and MT (machine
translation) will help here; since MT is slow, it helps if you can do
SUM in the foreign language, and then just do a quick and dirty MT on
the result, allowing either a human or an automated IR-based text
classifier to decide whether to keep or reject the article. For these