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Natural Language Technology is moving from text retrieval and search
applications to tasks that require genuine understanding of natural
language as well as the interaction between language understanding and
reasoning. Within the linguistic computational world a common
perspective has emerged on what is common to these natural language
understanding tasks under the heading "textual inferencing". The
aim is to develop systems that can decide when given two natural
language statements, what the inferential relation between the two
is. Textual inference simplifies the general language understanding
problem by limiting its interest to direct inferences avoiding
complicated chains of inferences and specialized world knowledge. But
even within this narrow conception of inferencing it remains to be
seen how we can get an inferential relation between two texts (e.g. a
question and an answer). Several approaches have tried to avoid deep
linguistic analysis altogether but others rely on a semantic
representation built on top of a syntactic parse. Within the latter
type of approaches there are alternatives both in the actual form that
the semantic representation takes and in the way it is arrived at. The
current workshop aims to present and discuss some of these
alternatives.
This March 9 -10 , 2012 event is sponsored by the National Science Foundation and the Center for the Study of Language and Information and organized by the "Language and Natural Reasoning" group at CSLI. Earlier recent workshops on similar topics and organized by members of the same research group can be found here and here . |