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With the emergence of broad-coverage parsers, quantitative evaluation
of parsers becomes
increasingly more important. We propose a dependency-based method for evaluating
broad-coverage parsers that offers more meaningful performance
measures than previous approaches.
We also present a structural pattern-matching mechanism that can be used
inconsequential differences among different parse trees. Previous evaluation
only evaluated the overall performance of parsers. The dependency-based
method can also
evaluate parsers with respect to different kinds of grammatical relationships
or different types
of lexical categories. An algorithm for transforming constituency trees
into dependency trees
is presented, which makes the evaluation method applicable to both constituency
and dependency grammars.
This paper describes tactical generation in Turkish, a
free constituent order language, in
which the order of the constituents may change according to the information
the sentences to be generated. In the absence of any information regarding
structure of a sentence (i.e. topic, focus, background, etc.), the constituents
of the sentence
obey a default order, but the order is almost freely changeable, depending
on the constraints
of the text flow or discourse. We have used a recursively structured finite
(much like a Recursive Transition Network (RTN)) for handling the changes
order, implemented as a right-linear grammar backbone. Our implementation
is the GenKit system, developed at Carnegie Mellon University, Center for
Translation. Morphological realization has been implemented using an external
analysis/generation component which performs concrete morpheme selection
This paper describes two experiments: one exploring the
amount of information relevant to
sense disambiguation contained in the part-of-speech field of entries in
a Machine Readable
Dictionary (MRD); the other, more practical, experiment attempts sense
all content words in a text assigning MRD homographs as sense tags using
part-of-speech information. We have implemented a simple
sense tagger which successfully tags 94%
of words using this method. A plan to extend this work and implement an
tagger is included.
This paper deals with the development of parsing techniques for
the analysis of natural
language sentences. We present a paradigm of a multi- path shift-reduce
combines two differently structured computational subsystems. The first
concerning native speakers' preferences, and the second
deals with the linguistic knowledge.
To apply preferences on parsing, we propose a method to rank the alternative
on the basis of parse context and frequency of use effects. The method
is mainly based
on psycholinguistic evidence, since we hope eventually to build a parser
working as closely
as possible to the way native speakers analyse natural sentences. We also
discuss in detail
techniques for optimizing the effectiveness of the proposed model. The
system has worked
successfully in parsing sentences in Modern Greek, a language where the
relatively free word
order characteristic results in many ambiguity problems. The proposed parsing
consistent with many directions in the field of preference-based parsing,
and it is proved to
be adequate in building effective and maintainable natural language analysers.
It is believed
that this model can also be used in parsing sentences in languages other