95,119 research outputs found
Semantic Ambiguity and Perceived Ambiguity
I explore some of the issues that arise when trying to establish a connection
between the underspecification hypothesis pursued in the NLP literature and
work on ambiguity in semantics and in the psychological literature. A theory of
underspecification is developed `from the first principles', i.e., starting
from a definition of what it means for a sentence to be semantically ambiguous
and from what we know about the way humans deal with ambiguity. An
underspecified language is specified as the translation language of a grammar
covering sentences that display three classes of semantic ambiguity: lexical
ambiguity, scopal ambiguity, and referential ambiguity. The expressions of this
language denote sets of senses. A formalization of defeasible reasoning with
underspecified representations is presented, based on Default Logic. Some
issues to be confronted by such a formalization are discussed.Comment: Latex, 47 pages. Uses tree-dvips.sty, lingmacros.sty, fullname.st
Forgetting Exceptions is Harmful in Language Learning
We show that in language learning, contrary to received wisdom, keeping
exceptional training instances in memory can be beneficial for generalization
accuracy. We investigate this phenomenon empirically on a selection of
benchmark natural language processing tasks: grapheme-to-phoneme conversion,
part-of-speech tagging, prepositional-phrase attachment, and base noun phrase
chunking. In a first series of experiments we combine memory-based learning
with training set editing techniques, in which instances are edited based on
their typicality and class prediction strength. Results show that editing
exceptional instances (with low typicality or low class prediction strength)
tends to harm generalization accuracy. In a second series of experiments we
compare memory-based learning and decision-tree learning methods on the same
selection of tasks, and find that decision-tree learning often performs worse
than memory-based learning. Moreover, the decrease in performance can be linked
to the degree of abstraction from exceptions (i.e., pruning or eagerness). We
provide explanations for both results in terms of the properties of the natural
language processing tasks and the learning algorithms.Comment: 31 pages, 7 figures, 10 tables. uses 11pt, fullname, a4wide tex
styles. Pre-print version of article to appear in Machine Learning 11:1-3,
Special Issue on Natural Language Learning. Figures on page 22 slightly
compressed to avoid page overloa
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