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The Penn Treebank has recently implemented a new syn- tactic annotation scheme, designed to highlight aspects of predicate-argument structure. This paper discusses the implementation of crucial aspects of this new …...

Alter presenting a novel O(n a) parsing al- gorithm for dependency grammar, we de- velop three contrasting ways to stochasticize it. We propose (a) a lexical atfinity mode] where words …...

In data-oriented language processing, an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new sentence is constructed by combining fragments from the corpus …...

The increasing availability of corpora anno- tated for linguistic structure prompts the question: if we have the same texts, anno- tated for phrase structure under two dif- ferent schemes, to …...

Treebanks, such as the Penn Treebank (PTB), offer a simple approach to obtaining a broad coverage grammar: one can simply read the grammar off the parse trees in the treebank. …...

Finding simple, non-recursive, base noun phrases is an important subtask for many natural language processing applications. While previous empirical methods for base NP identification have been rather complex, this paper …...

Three state-of-the-art statistical parsers are com- bined to produce more accurate parses, as well as new bounds on achievable Treebank parsing accu- racy. Two general approaches are presented and two …...

Computational learning of natural lan- guage is often attempted without using the knowledge available from other re- search areas such as psychology and linguistics. This can lead to systems that …...

This paper presents empirical studies and closely corresponding theoretical models of the performance of a chart parser exhaus- tively parsing the Penn Treebank with the Treebank’s own CFG grammar. We …...

We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and con- texts. Parameter search with EM produces higher quality analyses …...

This paper describes a simple pattern- matching algorithm for recovering empty nodes and identifying their co-indexed an- tecedents in phrase structure trees that do not contain this information. The pat- …...

This paper describes a wide-coverage sta- tistical parser that uses Combinatory Cat- egorial Grammar (CCG) to derive de- pendency structures. The parser differs from most existing wide-coverage tree- bank parsers …...

While the corpus-based research relies on hu- man annotated corpora, it is often said that a non-negligible amount of errors remain even in frequently used corpora such as Penn Treebank. …...

We describe an LR parser of parts-of- speech (and punctuation labels) for Tree Adjoining Grammars (TAGs), that solves table conflicts in a greedy way, with lim- ited amount of backtracking. …...

This paper describes new default unification, lenient default unification. It works efficiently, and gives more informative results because it maximizes the amount of information in the result, while other de- …...

We present an extension of the classic A* search procedure to tabular PCFG parsing. The use of A* search can dramatically reduce the time required to find a best parse …...

We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag con- texts via a dependency network representa- tion, (ii) broad …...

We present a neural network method for induc- ing representations of parse histories and us- ing these history representations to estimate the probabilities needed by a statistical left-corner parser. The …...

We present a neural-network-based sta- tistical parser, trained and tested on the Penn Treebank. The neural net- work is used to estimate the parame- ters of a generative model of …...

This paper presents a deterministic dependency parser based on memory-based learning, which parses English text in linear time. When trained and evaluated on the Wall Street Journal sec- tion of …...

This paper discusses the use of statistical word alignment over multiple parallel texts for the identi- fication of string spans that cannot be constituents in one of the languages. This …...

This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of …...

We present several statistical models of syntactic constituent order for sentence realization. We compare several models, including simple joint models inspired by existing statistical parsing models, and several novel conditional …...

This paper describes and evaluates log-linear parsing models for Combinatory Categorial Grammar (CCG). A parallel implementation of the L-BFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing …...

This work presents a type of parser that takes the process of chunking to the stage of producing full parse trees. This type of parser, denoted Thin Parsers (TP) in …...

The annotations of the Penn Discourse Treebank (PDTB) include (1) discourse connectives and their arguments, and (2) attribution of each argument of each con- nective and of the relation it …...

We present an automatic approach to tree annota- tion in which basic nonterminal symbols are alter- nately split and merged to maximize the likelihood of a training treebank. Starting with …...

In this work we learn clusters of contex- tual annotations for non-terminals in the Penn Treebank. Perhaps the best way to think about this problem is to contrast our work …...

We identify problems with the Penn Tree- bank that render it imperfect for syntax- based machine translation and propose methods of relabeling the syntax trees to improve translation quality. We …...

This paper describes a parser which gen- erates parse trees with empty elements in which traces and fillers are co-indexed. The parser is an unlexicalized PCFG parser which is guaranteed …...

The Penn Treebank does not annotate within base noun phrases (NPs), commit- ting only to flat structures that ignore the complexity of English NPs. This means that tools trained on …...

We examine the problem of choosing word order for a set of dependency trees so as to minimize total dependency length. We present an algorithm for computing the op- timal …...

Graph-based and transition-based approaches to dependency parsing adopt very different views of the problem, each view having its own strengths and limitations. We study both approaches under the framework of …...

Statistical parsing of noun phrase (NP) struc- ture has been hampered by a lack of gold- standard data. This is a significant problem for CCGbank, where binary branching NP deriva- …...

This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning ap- proach. We describe an extension of semi- supervised structured conditional models (SS-SCMs) to the …...

AbstractThe aim of this paper is twofold. We focus, on the one hand, on the task of dynamically annotating English compound nouns, and on the other hand we propose disambiguation …...

We present the first evaluation of the utility of automatic evaluation metrics on surface real- izations of Penn Treebank data. Using outputs of the OpenCCG and XLE realizers, along with …...

AbstractWe investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and …...

Resolving coordination ambiguity is a clas- sic hard problem. This paper looks at co- ordination disambiguation in complex noun phrases (NPs). Parsers trained on the Penn Treebank are reporting impressive …...

AbstractWe introduce a substantial update of the Prague Czech-English Dependency Treebank, a parallel corpus manually annotated at the deep syntactic layer of linguistic representation. The English part consists of the …...

Combinatory Categorial Grammar (CCG) is an expressive grammar formalism which is able to capture long-range dependencies. However, building large and wide-coverage treebanks for CCG is expensive and time-consuming. In this …...

AbstractIt is well known that accuracies of statistical parsers trained over Penn Treebank on test sets drawn from the same corpus tend to be overestimates of their actual parsing performance. …...

We investigate the stacking of dependency and phrase structure parsers, i.e. we define features from the output of a phrase structure parser for a dependency parser and vice versa. Our …...

We investigate the problem of automatically converting from a dependency representa- tion to a phrase structure representation, a key aspect of understanding the relationship between these two representations for NLP …...

This paper presents a novel method using graph-based semi-supervised learning (SSL) to improve the syntax parsing of unknown words. Different from conventional approaches that uses hand-crafted rules, rich morphological features, …...

In this paper, we consider the problem of cross-formalism transfer in parsing. We are interested in parsing constituency- based grammars such as HPSG and CCG using a small amount of …...

We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventions informed by the …...

We develop an instance (token) based extension of the state of the art word (type) based part-of- speech induction system introduced in (Yatbaz et al., 2012). Each word instance is …...

Coordination is an important and common syntactic construction which is not han- dled well by state of the art parsers. Co- ordinations in the Penn Treebank are miss- ing internal …...

State-of-the-art sequence labeling systems traditionally require large amounts of task- specific knowledge in the form of hand- crafted features and data pre-processing. In this paper, we introduce a novel neu- …...

AbstractNeural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a …...

In this work, we propose a novel method to incorporate corpus-level discourse infor- mation into language modelling. We call this larger-context language model. We in- troduce a late fusion approach …...

The goal of language modeling techniques is to capture the statistical and structural prop- erties of natural languages from training cor- pora. This task typically involves the learning of short …...

AbstractDiscourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing. Most attempts at this problem rely on annotated text segmentations (Elementary Discourse Units, EDUs) …...

AbstractNeural language models are usually trained using Maximum-Likelihood Estimation (MLE). The corresponding objective function for MLE is derived from the Kullback-Leibler (KL) divergence between the empirical probability distribution representing the …...

AbstractHead-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating …...

AbstractIt is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structure into neural language models has been a challenging topic. …...

AbstractBoth syntactic and semantic structures are key linguistic contextual clues, in which parsing the latter has been well shown beneficial from parsing the former. However, few works ever made an …...

AbstractAttention mechanisms have improved the performance of NLP tasks while allowing models to remain explainable. Self-attention is currently widely used, however interpretability is difficult due to the numerous attention distributions. …...

The combined use of neural scoring systems and BERT fine-tuning has led to very high results in many natural language processing (NLP) tasks. These high results raise two im- portant …...

We propose two fast neural combinatory mod- els for constituency parsing: binary and multi- branching. Our models decompose the bottom- up parsing process into 1) classification of tags, labels, and …...

AbstractIn recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks. But, in the unsupervised POS tagging task, works utilizing PLMs are few and fail …...