462 research outputs found
Shift-Reduce CCG Parsing with a Dependency Model
This paper presents the first dependency model for a shift-reduce CCG parser. Modelling dependencies is desirable for a number of reasons, including handling the “spurious ” ambiguity of CCG; fitting well with the theory of CCG; and optimizing for structures which are evaluated at test time. We develop a novel training technique using a dependency oracle, in which all derivations are hidden. A challenge arises from the fact that the oracle needs to keep track of exponentially many goldstandard derivations, which is solved by integrating a packed parse forest with the beam-search decoder. Standard CCGBank tests show the model achieves up to 1.05 labeled F-score improvements over three existing, competitive CCG parsing models
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Structured Learning with Inexact Search: Advances in Shift-Reduce CCG Parsing
Statistical shift-reduce parsing involves the interplay of representation learning, structured learning, and inexact search. This dissertation considers approaches that tightly integrate these three elements and explores three novel models for shift-reduce CCG parsing. First, I develop a dependency model, in which the selection of shift-reduce action sequences producing a dependency structure is treated as a hidden variable; the key components of the model are a dependency oracle and a learning algorithm that integrates the dependency oracle, the structured perceptron, and beam search. Second, I present expected F-measure training and show how to derive a globally normalized RNN model, in which beam search is naturally incorporated and used in conjunction with the
objective to learn shift-reduce action sequences optimized for the final evaluation metric. Finally, I describe an LSTM model that is able to construct parser state representations incrementally by following the shift-reduce syntactic derivation process; I show expected F-measure training, which is agnostic to the underlying neural network, can be applied in this setting to obtain globally normalized greedy and beam-search LSTM shift-reduce parsers.The Carnegie Trust for the Universities of Scotland;
The Cambridge Trus
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 “John ate an apple”, parsers based on the
widely used dependency grammars find grammatical relations, such as that ‘John’ is
the subject and ‘apple’ is the object of the action ‘ate’. 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
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201
A syntactic language model based on incremental CCG parsing
Syntactically-enriched language models (parsers) constitute a promising component in applications such as machine translation and speech-recognition. To maintain a useful level of accuracy, existing parsers are non-incremental and must span a combinatorially growing space of possible structures as every input word is processed. This prohibits their incorporation into standard linear-time decoders. In this paper, we present an incremental, linear-time dependency parser based on Combinatory Categorial Grammar (CCG) and classification techniques. We devise a deterministic transform of CCGbank canonical derivations into incremental ones, and train our parser on this data. We discover that a cascaded, incremental version provides an appealing balance between efficiency and accuracy
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