3,488 research outputs found
Unsupervised Semantic Role Induction via Split-Merge Clustering
In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acquisition bottleneck associated with supervised role labelers. We present an algorithm that iteratively splits and merges clusters representing semantic roles, thereby leading from an initial clustering to a final clustering of better quality. The method is simple, surprisingly effective, and allows to integrate linguistic knowledge transparently. By combining role induction with a rule-based component for argument identification we obtain an unsupervised end-to-end semantic role labeling system. Evaluation on the CoNLL 2008 benchmark dataset demonstrates that our method outperforms competitive unsupervised approaches by a wide margin.
Minimal supervision for language learning: bootstrapping global patterns from local knowledge
A fundamental step in sentence comprehension involves assigning semantic roles
to sentence constituents. To accomplish this, the listener
must parse the sentence, find constituents that are candidate arguments, and
assign semantic roles to those constituents. Each step depends on prior lexical
and syntactic knowledge. Where do children begin in solving this problem when
learning their first languages? To experiment with different representations
that children may use to begin understanding language, we have built a
computational model for this early point in language acquisition. This system,
BabySRL, learns from transcriptions of natural child-directed speech and makes
use of psycholinguistically plausible background knowledge and realistically
noisy semantic feedback to begin to classify sentences at the level of ``who
does what to whom.''
Starting with simple, psycholinguistically-motivated representations of
sentence structure, the BabySRL is able to learn from full semantic feedback,
as well as a supervision signal derived from partial semantic background
knowledge. In addition we combine the BabySRL with an unsupervised Hidden
Markov Model part-of-speech tagger, linking clusters with syntactic categories
using background noun knowledge so that they can be used to parse input for the
SRL system. The results show that proposed shallow representations of sentence
structure are robust to reductions in parsing accuracy, and that the
contribution of alternative representations of sentence structure to successful
semantic role labeling varies with the integrity of the parsing and
argument-identification stages. Finally, we enable the BabySRL to improve both
an intermediate syntactic representation and its final semantic role
classification. Using this system we show that it is possible for a simple
learner in a plausible (noisy) setup to begin comprehending simple semantics
when initialized with a small amount of concrete noun knowledge and some simple
syntax-semantics mapping biases, before acquiring any specific verb knowledge
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