95 research outputs found
Unsupervised Statistical Learning of Context-free Grammar
In this paper, we address the problem of inducing (weighted) context-free grammar (WCFG) on data given.
The induction is performed by using a new model of grammatical inference, i.e., weighted Grammar-based
Classifier System (wGCS). wGCS derives from learning classifier systems and searches grammar structure
using a genetic algorithm and covering. Weights of rules are estimated by using a novelty Inside-Outside
Contrastive Estimation algorithm. The proposed method employs direct negative evidence and learns WCFG
both form positive and negative samples. Results of experiments on three synthetic context-free languages
show that wGCS is competitive with other statistical-based method for unsupervised CFG learning
Learning Efficient Disambiguation
This dissertation analyses the computational properties of current
performance-models of natural language parsing, in particular Data Oriented
Parsing (DOP), points out some of their major shortcomings and suggests
suitable solutions. It provides proofs that various problems of probabilistic
disambiguation are NP-Complete under instances of these performance-models, and
it argues that none of these models accounts for attractive efficiency
properties of human language processing in limited domains, e.g. that frequent
inputs are usually processed faster than infrequent ones. The central
hypothesis of this dissertation is that these shortcomings can be eliminated by
specializing the performance-models to the limited domains. The dissertation
addresses "grammar and model specialization" and presents a new framework, the
Ambiguity-Reduction Specialization (ARS) framework, that formulates the
necessary and sufficient conditions for successful specialization. The
framework is instantiated into specialization algorithms and applied to
specializing DOP. Novelties of these learning algorithms are 1) they limit the
hypotheses-space to include only "safe" models, 2) are expressed as constrained
optimization formulae that minimize the entropy of the training tree-bank given
the specialized grammar, under the constraint that the size of the specialized
model does not exceed a predefined maximum, and 3) they enable integrating the
specialized model with the original one in a complementary manner. The
dissertation provides experiments with initial implementations and compares the
resulting Specialized DOP (SDOP) models to the original DOP models with
encouraging results.Comment: 222 page
Parsing Inside-Out
The inside-outside probabilities are typically used for reestimating
Probabilistic Context Free Grammars (PCFGs), just as the forward-backward
probabilities are typically used for reestimating HMMs. I show several novel
uses, including improving parser accuracy by matching parsing algorithms to
evaluation criteria; speeding up DOP parsing by 500 times; and 30 times faster
PCFG thresholding at a given accuracy level. I also give an elegant,
state-of-the-art grammar formalism, which can be used to compute inside-outside
probabilities; and a parser description formalism, which makes it easy to
derive inside-outside formulas and many others.Comment: Ph.D. Thesis, 257 pages, 40 postscript figure
Improving Compositional Generalization with Latent Structure and Data Augmentation
Generic unstructured neural networks have been shown to struggle on
out-of-distribution compositional generalization. Compositional data
augmentation via example recombination has transferred some prior knowledge
about compositionality to such black-box neural models for several semantic
parsing tasks, but this often required task-specific engineering or provided
limited gains.
We present a more powerful data recombination method using a model called
Compositional Structure Learner (CSL). CSL is a generative model with a
quasi-synchronous context-free grammar backbone, which we induce from the
training data. We sample recombined examples from CSL and add them to the
fine-tuning data of a pre-trained sequence-to-sequence model (T5). This
procedure effectively transfers most of CSL's compositional bias to T5 for
diagnostic tasks, and results in a model even stronger than a T5-CSL ensemble
on two real world compositional generalization tasks. This results in new
state-of-the-art performance for these challenging semantic parsing tasks
requiring generalization to both natural language variation and novel
compositions of elements.Comment: NAACL 202
Statistical Deep parsing for spanish
This document presents the development of a statistical HPSG parser for Spanish. HPSG is a deep linguistic formalism that combines syntactic and semanticinformation in the same representation, and is capable of elegantly modelingmany linguistic phenomena. Our research consists in the following steps: design of the HPSG grammar, construction of the corpus, implementation of theparsing algorithms, and evaluation of the parsers performance. We created a simple yet powerful HPSG grammar for Spanish that modelsmorphosyntactic information of words, syntactic combinatorial valence, and semantic argument structures in its lexical entries. The grammar uses thirteenvery broad rules for attaching specifiers, complements, modifiers, clitics, relative clauses and punctuation symbols, and for modeling coordinations. In asimplification from standard HPSG, the only type of long range dependency wemodel is the relative clause that modifies a noun phrase, and we use semanticrole labeling as our semantic representation. We transformed the Spanish AnCora corpus using a semi-automatic processand analyzed it using our grammar implementation, creating a Spanish HPSGcorpus of 517,237 words in 17,328 sentences (all of AnCora). We implemented several statistical parsing algorithms and trained them overthis corpus. The implemented strategies are: a bottom-up baseline using bi-lexical comparisons or a multilayer perceptron; a CKY approach that uses theresults of a supertagger; and a top-down approach that encodes word sequencesusing a LSTM network. We evaluated the performance of the implemented parsers and compared them with each other and against other existing Spanish parsers. Our LSTM top-down approach seems to be the best performing parser over our test data, obtaining the highest scores (compared to our strategies and also to externalparsers) according to constituency metrics (87.57 unlabeled F1, 82.06 labeled F1), dependency metrics (91.32 UAS, 88.96 LAS), and SRL (87.68 unlabeled,80.66 labeled), but we must take in consideration that the comparison against the external parsers might be noisy due to the post-processing we needed to do in order to adapt them to our format. We also defined a set of metrics to evaluate the identification of some particular language phenomena, and the LSTM top-down parser out performed the baselines in almost all of these metrics as well.Este documento presenta el desarrollo de un parser HPSG estadístico para el español. HPSG es un formalismo lingüístico profundo que combina información sintáctica y semántica en sus representaciones, y es capaz de modelar elegantemente una buena cantidad de fenómenos lingüísticos. Nuestra investigación se compone de los siguiente pasos: diseño de la gramática HPSG, construcción del corpus, implementación de los algoritmos de parsing y evaluación de la performance de los parsers. Diseñamos una gramática HPSG para el español simple y a la vez poderosa, que modela en sus entradas léxicas la información morfosintáctica de las palabras, la valencia combinatoria sintáctica y la estructura argumental semántica. La gramática utiliza trece reglas genéricas para adjuntar especificadores, complementos, clíticos, cláusulas relativas y símbolos de puntuación, y también para modelar coordinaciones. Como simplificación de la teoría HPSG estándar, el único tipo de dependencia de largo alcance que modelamos son las cláusulas relativas que modifican sintagmas nominales, y utilizamos etiquetado de roles semánticos como representación semántica. Transformamos el corpus AnCora en español utilizando un proceso semiautomático y lo analizamos mediante nuestra implementación de la gramática, para crear un corpus HPSG en español de 517,237 palabras en 17,328 oraciones (todo el contenido de AnCora). Implementamos varios algoritmos de parsing estadístico entrenados sobre este corpus. En particular, teníamos como objetivo probar enfoques basados en redes neuronales. Las estrategias implementadas son: una línea base bottom-up que utiliza comparaciones bi-léxicas o un perceptrón multicapa; un enfoque tipo CKY que utiliza los resultados de un supertagger; y un enfoque top-down que codifica las secuencias de palabras mediante redes tipo LSTM. Evaluamos la performance de los parsers implementados y los comparamos entre sí y con un conjunto de parsers existententes para el español. Nuestro enfoque LSTM top-down parece ser el que tiene mejor desempeño para nuestro conjunto de test, obteniendo los mejores puntajes (comparado con nuestras estrategias y también con parsers externos) en cuanto a métricas de constituyentes (87.57 F1 no etiquetada, 82.06 F1 etiquetada), métricas de dependencias (91.32 UAS, 88.96 LAS), y SRL (87.68 no etiquetada, 80.66 etiquetada), pero debemos tener en cuenta que la comparación con parsers externos puede ser ruidosa debido al post procesamiento realizado para adaptarlos a nuestro formato. También definimos un conjunto de métricas para evaluar la identificación de algunos fenómenos particulares del lenguaje, y el parser LSTM top-down obtuvo mejores resultados que las baselines para casi todas estas métricas
Neural Combinatory Constituency Parsing
東京都立大学Tokyo Metropolitan University博士(情報科学)doctoral thesi
Polynomial Time Algorithms for Multi-Type Branching Processes and Stochastic Context-Free Grammars
We show that one can approximate the least fixed point solution for a
multivariate system of monotone probabilistic polynomial equations in time
polynomial in both the encoding size of the system of equations and in
log(1/\epsilon), where \epsilon > 0 is the desired additive error bound of the
solution. (The model of computation is the standard Turing machine model.)
We use this result to resolve several open problems regarding the
computational complexity of computing key quantities associated with some
classic and heavily studied stochastic processes, including multi-type
branching processes and stochastic context-free grammars
Integrated supertagging and parsing
EuroMatrixPlus project funded by the European Commission, 7th Framework ProgrammeParsing is the task of assigning syntactic or semantic structure to a natural language
sentence. This thesis focuses on syntactic parsing with Combinatory Categorial Grammar
(CCG; Steedman 2000). CCG allows incremental processing, which is essential
for speech recognition and some machine translation models, and it can build semantic
structure in tandem with syntactic parsing. Supertagging solves a subset of the parsing
task by assigning lexical types to words in a sentence using a sequence model. It has
emerged as a way to improve the efficiency of full CCG parsing (Clark and Curran,
2007) by reducing the parser’s search space. This has been very successful and it is the
central theme of this thesis.
We begin by an analysis of how efficiency is being traded for accuracy in supertagging.
Pruning the search space by supertagging is inherently approximate and to contrast
this we include A* in our analysis, a classic exact search technique. Interestingly,
we find that combining the two methods improves efficiency but we also demonstrate
that excessive pruning by a supertagger significantly lowers the upper bound on accuracy
of a CCG parser.
Inspired by this analysis, we design a single integrated model with both supertagging
and parsing features, rather than separating them into distinct models chained
together in a pipeline. To overcome the resulting complexity, we experiment with both
loopy belief propagation and dual decomposition approaches to inference, the first empirical
comparison of these algorithms that we are aware of on a structured natural
language processing problem.
Finally, we address training the integrated model. We adopt the idea of optimising
directly for a task-specific metric such as is common in other areas like statistical
machine translation. We demonstrate how a novel dynamic programming algorithm
enables us to optimise for F-measure, our task-specific evaluation metric, and experiment
with approximations, which prove to be excellent substitutions.
Each of the presented methods improves over the state-of-the-art in CCG parsing.
Moreover, the improvements are additive, achieving a labelled/unlabelled dependency
F-measure on CCGbank of 89.3%/94.0% with gold part-of-speech tags, and
87.2%/92.8% with automatic part-of-speech tags, the best reported results for this task
to date. Our techniques are general and we expect them to apply to other parsing problems,
including lexicalised tree adjoining grammar and context-free grammar parsing
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