Parsing Accuracy

We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to re- solve ambiguity. HBG incorporates lexical, syn- tactic, semantic, …...

This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability es- timation techniques are extended to calcu- …...

A major concern in corpus based ap- proaches is that the applicability of the ac- quired knowledge may be limited by some feature of the corpus, in particular, the no- …...

This l)al)er describes novel and practical .lal)anesc parsers that uses decision trees, l"irst, we COl> struct a single, decision tree to estimate modifica-- lion probabilities; how one phrase tends t.o …...

This paper describes novel and practical Japanese parsers that uses decision trees. First, we con- struct a single decision tree to estimate modifica- tion probabilities; how one phrase tends to …...

This paper describes a hybrid parsing method for Japanese which uses both a hand-crafted gram- mar and a statistical technique. The key feature of our system is that in order …...

We study the impact of richer syntac- tic dependencies on the performance of the structured language model (SLM) along three dimensions: parsing accu- racy (LP/LR), perplexity (PPL) and word- error-rate …...

To investigate the contributions of tag- gers or chunkers to the performance of a deep syntactic parser, Weighted Constraint Dependency Grammars have been extended to also take into con- sideration …...

Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using Combinatory Categorial Grammar (CCG). However, overall figures give no indication of a parser’s performance on …...

. This paper describes a method of adapting a domain-inde- pendent HPSG parser to a biomedical domain. Without modifying the grammar and the probabilistic model of the original HPSG parser, …...

In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective depen- dency structures. We show how a data- driven deterministic dependency parser, in …...

Transforming syntactic representations in order to improve parsing accuracy has been exploited successfully in statistical parsing systems using constituency-based representations. In this paper, we show that similar transformations can give …...

We investigate the utility of supertag infor- mation for guiding an existing dependency parser of German. Using weighted con- straints to integrate the additionally avail- able information, the decision process …...

In this paper, we present a framework for multi-lingual dependency parsing. Our bottom-up deterministic parser adopts Nivre’s algorithm (Nivre, 2004) with a preprocessor. Support Vector Machines (SVMs) are utilized to …...

We introduce a framework for syntactic parsing with latent variables based on a form of dynamic Sigmoid Belief Networks called Incremental Sigmoid Belief Networks. We demonstrate that a previous feed-forward …...

In this paper, we consider classifying word positions by whether or not they can either start or end multi-word constituents. This provides a mechanism for “closing” chart cells during context-free …...

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for …...

We present a discriminative, latent variable approach to syntactic parsing in which rules exist at multiple scales of refinement. The model is formally a latent variable CRF gram- mar over …...

Many different types of features have been shown to improve accuracy in parse reranking. A class of features that thus far has not been considered is based on a pro- …...

We present several algorithms for assign- ing heads in phrase structure trees, based on different linguistic intuitions on the role of heads in natural language syntax. Start- ing point of …...

AbstractThe creation of language resources for less-resourced languages like the historical ones benefits from the exploitation of language-independent tools and methods developed over the years by many projects for modern …...

We show that the automatically induced latent variable grammars of Petrov et al. (2006) vary widely in their underlying representations, de- pending on their EM initialization point. We use this …...

It is well known that parsing accuracy suf- fers when a model is applied to out-of-domain data. It is also known that the most benefi- cial data to parse a …...

Part-of-speech (POS) is an indispensable fea- ture in dependency parsing. Current research usually models POS tagging and dependency parsing independently. This may suffer from error propagation problem. Our experiments show …...

We report on our ongoing work in devel- oping the Irish Dependency Treebank, de- scribe the results of two Inter-annotator Agreement (IAA) studies, demonstrate improvements in annotation consistency which have …...

Graph-based dependency parsers suffer from the sheer number of higher order edges they need to (a) score and (b) consider during opti- mization. Here we show that when working with …...

In this paper, we show an approach to ex- tracting different types of constraint rules from a dependency treebank. Also, we show an approach to integrating these con- straint rules …...

AbstractWe introduce the Denoised Web Treebank: a treebank including a normalization layer and a corresponding evaluation metric for dependency parsing of noisy text, such as Tweets. This benchmark enables the …...

We adapt the greedy stack LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dy- namic oracles (Goldberg and Nivre, 2013) in- stead of assuming …...

AbstractWe present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal …...

In this work we focus on a particular linguistic phenomenon, ellipsis, and explore the latest parsers in order to learn about parsing accuracy and typical errors from the perspective of …...

AbstractIn unsupervised grammar induction, data likelihood is known to be only weakly correlated with parsing accuracy, especially at convergence after multiple runs. In order to find a better indicator for …...

AbstractIn Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language …...

AbstractMannual annotation for dependency parsing is both labourious and time costly, resulting in the difficulty to learn practical dependency parsers for many languages due to the lack of labelled training …...

AbstractIn recent years, parsing performance is dramatically improved on in-domain texts thanks to the rapid progress of deep neural network models. The major challenge for current parsing research is to …...

AbstractIn this paper we describe and evaluate syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, as well as tools and methods used in the production …...

AbstractStandard models for syntactic dependency parsing take words to be the elementary units that enter into dependency relations. In this paper, we investigate whether there are any benefits from enriching …...

AbstractComplex Question Understanding (CQU) parses complex questions to Question Decomposition Meaning Representation (QDMR) which is a sequence of atomic operators. Existing works are based on end-to-end neural models which do …...