6 research outputs found
Transition-based combinatory categorial grammar parsing for English and Hindi
Given a natural language sentence, parsing is the task of assigning it a grammatical
structure, according to the rules within a particular grammar formalism. Different
grammar formalisms like Dependency Grammar, Phrase Structure Grammar, Combinatory
Categorial Grammar, Tree Adjoining Grammar are explored in the literature for
parsing. For example, given a sentence like âJohn ate an appleâ, parsers based on the
widely used dependency grammars find grammatical relations, such as that âJohnâ is
the subject and âappleâ is the object of the action âateâ. We mainly focus on Combinatory
Categorial Grammar (CCG) in this thesis.
In this thesis, we present an incremental algorithm for parsing CCG for two diverse
languages: English and Hindi. English is a fixed word order, SVO (Subject-Verb-
Object), and morphologically simple language, whereas, Hindi, though predominantly
a SOV (Subject-Object-Verb) language, is a free word order and morphologically rich
language. Developing an incremental parser for Hindi is really challenging since the
predicate needed to resolve dependencies comes at the end. As previously available
shift-reduce CCG parsers use English CCGbank derivations which are mostly right
branching and non-incremental, we design our algorithm based on the dependencies
resolved rather than the derivation. Our novel algorithm builds a dependency graph in
parallel to the CCG derivation which is used for revealing the unbuilt structure without
backtracking. Though we use dependencies for meaning representation and CCG for
parsing, our revealing technique can be applied to other meaning representations like
lambda expressions and for non-CCG parsing like phrase structure parsing.
Any statistical parser requires three major modules: data, parsing algorithm and
learning algorithm. This thesis is broadly divided into three parts each dealing with
one major module of the statistical parser. In Part I, we design a novel algorithm
for converting dependency treebank to CCGbank. We create Hindi CCGbank with a
decent coverage of 96% using this algorithm. We also do a cross-formalism experiment
where we show that CCG supertags can improve widely used dependency parsers.
We experiment with two popular dependency parsers (Malt and MST) for two diverse
languages: English and Hindi. For both languages, CCG categories improve the overall
accuracy of both parsers by around 0.3-0.5% in all experiments. For both parsers,
we see larger improvements specifically on dependencies at which they are known
to be weak: long distance dependencies for Malt, and verbal arguments for MST.
The result is particularly interesting in the case of the fast greedy parser (Malt), since
improving its accuracy without significantly compromising speed is relevant for large
scale applications such as parsing the web.
We present a novel algorithm for incremental transition-based CCG parsing for
English and Hindi, in Part II. Incremental parsers have potential advantages for applications
like language modeling for machine translation and speech recognition. We
introduce two new actions in the shift-reduce paradigm for revealing the required information
during parsing. We also analyze the impact of a beam and look-ahead for
parsing. In general, using a beam and/or look-ahead gives better results than not using
them. We also show that the incremental CCG parser is more useful than a non-incremental
version for predicting relative sentence complexity. Given a pair of sentences
from wikipedia and simple wikipedia, we build a classifier which predicts if one
sentence is simpler/complex than the other. We show that features from a CCG parser
in general and incremental CCG parser in particular are more useful than a chart-based
phrase structure parser both in terms of speed and accuracy.
In Part III, we develop the first neural network based training algorithm for parsing
CCG. We also study the impact of neural network based tagging models, and greedy
versus beam-search parsing, by using a structured neural network model. In greedy
settings, neural network models give significantly better results than the perceptron
models and are also over three times faster. Using a narrow beam, structured neural
network model gives consistently better results than the basic neural network model.
For English, structured neural network gives similar performance to structured perceptron
parser. But for Hindi, structured perceptron is still the winner
Head-Driven Phrase Structure Grammar
Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)
Head-Driven Phrase Structure Grammar
Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)
Tune your brown clustering, please
Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal