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Grammar Induction
Position
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#249275
Grammatical induction, also known as grammatical inference or syntactic pattern recognition
From
Wikipedia
:
Grammatical induction
, also known as
grammatical inference
or
syntactic pattern recognition
, refers to the process in
machine learning
of learning a
formal grammar
(usually in the form of
re-write rules
or
productions
) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects. Grammatical inference is distinguished from traditional
decision rules
and other such methods principally by the nature of the resulting model, which in the case of grammatical inference relies heavily on hierarchical substitutions. Whereas a traditional decision rule set is geared toward assessing object classification, a grammatical rule set is geared toward the generation of examples. In this sense, the grammatical-induction problem can be said to seek a
generative
model, while the decision-rule problem seeks a
descriptive
model.
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[1]
Unsupervised Multilingual Grammar Induction
Author:
Benjamin Snyder, Tahira Naseem, Regina Barzilay
Publication info:
2009 ACL 09
Cited by:
Jack Park
5:20 AM 5 February 2013 GMT
URL:
http://people.csail.mit.edu/tahira/acl09.pdf
Excerpt / Summary
We investigate the task of unsupervised constituency parsing from bilingual parallel corpora. Our goal is to use bilingual cues to learn improved parsing models for each language and to evaluate these models on held-out monolingual test data. We formulate a generative Bayesian model which seeks to explain the observed parallel data through a combination of bilingual and monolingual parameters. To this end, we adapt a formalism known as unordered tree alignment to our probabilistic setting. Using this formalism, our model loosely binds parallel trees while allowing language-specific syntactic structure. We perform inference under this model using Markov Chain Monte Carlo and dynamic programming. Applying this model to three parallel corpora (Korean-English, Urdu-English, and Chinese-English) we find substantial performance gains over the CCM model, a strong monolingual baseline. On average, across a variety of testing scenarios, our model achieves an 8.8 absolute gain in F-measure.
Link
[2]
Automatic Grammar Induction and Parsing Free Text: A Transformation-Based Approach
Author:
Eric Brill
Publication info:
1993 HLT '93
Cited by:
Jack Park
5:27 AM 5 February 2013 GMT
URL:
http://acl.ldc.upenn.edu/H/H93/H93-1047.pdf
Excerpt / Summary
In this paper we describe a new technique for parsing free text: a transformational grammar I is automatically learned that is capable of accurately parsing text into binary-branching syntactic trees with nonterminals unlabelled. The algorithm works by beginning in a very naive state of knowledge about phrase structure. By repeatedly comparing the results of bracketing in the current state to proper bracketing provided in the training corpus, the system learns a set of simple structural transformations that can be applied to reduce error. After describing the algorithm, we present results and compare these results to other recent results in automatic grammar induction.
Link
[3]
Natural Language Grammar Induction using a Constituent-Context Model
Author:
Dan Klein, Christopher D. Manning
Publication info:
2005 Pattern Recognition Volume 38 Issue 9, September, 2005
Cited by:
Jack Park
5:33 AM 5 February 2013 GMT
URL:
http://www.cs.berkeley.edu/~klein/papers/klein_and_manning-constituent_context_model-NIPS_2001.pdf
Excerpt / Summary
This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according to generative PCFG models. In contrast, we employ a simpler probabilistic model over trees based directly on constituent identity and linear context, and use an EM-like iterative procedure to induce structure. This method produces much higher quality analyses, giving the best published results on the ATIS dataset.
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