A94-1012 into the tool kit for linguistic knowledge acquisition which we are now developing .
A92-1025 new ways around old problem : in knowledge acquisition . This paper explains the relationship
A88-1007 adopt a paradigm of incremental knowledge acquisition . Our incremental approach is
A00-1037 may be used . 5 Conclusions The knowledge acquisition technology described above is
A00-1037 from texts ( Woods 1997 ) . The Knowledge Acquisition from Text ( KAT ) system is presented
A92-1025 rather than a replacement for knowledge acquisition . The next section describes
A92-1025 statistical methods for automated knowledge acquisition . Published work on text categorization
A00-2019 formulation was that we could bypass the knowledge acquisition bottleneck . All occurrences
A97-1055 statistically-based approaches to lexical knowledge acquisition are faced with the problem of
A00-2023 1995 ) suggested overcoming the knowledge acquisition bottleneck in generation by tapping
A94-1012 strong similarity . The grammatical knowledge acquisition method proposed in this paper
A00-1037 increase the automation of the knowledge acquisition system . Without a good handling
A88-1000 with an emphasis on interactive knowledge acquisition tools that facilitate the task
A92-1045 Developers need help in the various knowledge acquisition tasks , such as dictionary and
A88-1000 / interactive edit - ing . The knowledge acquisition needs of an MT system , with
A92-1014 do n't need to distinguish the knowledge acquisition phase from the phase of using
A83-1002 major problems in this area are knowledge acquisition and representa - tion . For many
C00-1002 Conclusion We described a linguistic knowledge acquisition model and tested it ; on a word
A00-2043 course , by the limits of the knowledge acquisition devices ) , to the entire domain
A92-1032 trigger a bootstrapping process of knowledge acquisition , giving us some chance to overcome
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