The exploration of deterministic and efficient dependency parsing (original) (raw)

Investigating multilingual dependency parsing

Proceedings of the Tenth Conference on Computational Natural Language Learning - CoNLL-X '06, 2006

In this paper, we describe a system for the CoNLL-X shared task of multilingual dependency parsing. It uses a baseline Nivre's parser (Nivre, 2003) that first identifies the parse actions and then labels the dependency arcs. These two steps are implemented as SVM classifiers using LIBSVM. Features take into account the static context as well as relations dynamically built during parsing. We experimented two main additions to our implementation of Nivre's parser: Nbest search and bidirectional parsing. We trained the parser in both left-right and right-left directions and we combined the results. To construct a single-head, rooted, and cycle-free tree, we applied the Chu-Liu/Edmonds optimization algorithm. We ran the same algorithm with the same parameters on all the languages.

Multi-lingual dependency parsing at NAIST

Proceedings of the Tenth Conference on Computational Natural Language Learning - CoNLL-X '06, 2006

In this paper, we present a framework for multilingual dependency parsing. Our bottom-up deterministic parser adopts Nivre's algorithm (Nivre, 2004) with a preprocessor. Support Vector Machines (SVMs) are utilized to determine the word dependency attachments. Then, a maximum entropy method (MaxEnt) is used for determining the label of the dependency relation. To improve the performance of the parser, we construct a tagger based on SVMs to find neighboring attachment as a preprocessor. Experimental evaluation shows that the proposed extension improves the parsing accuracy of our base parser in 9 languages.

A System for Experiments with Dependency Parsers

2014

In this paper we present a system for experimenting with combinations of dependency parsers. The system supports initial training of different parsing models, creation of parsebank(s) with these models, and different strategies for the construction of ensemble models aimed at improving the output of the individual models by voting. The system employs two algorithms for construction of dependency trees from several parses of the same sentence and several ways for ranking of the arcs in the resulting trees. We have performed experiments with state-of-the-art dependency parsers including MaltParser, MSTParser, TurboParser, and MATEParser, on the data from the Bulgarian treebank -- BulTreeBank. Our best result from these experiments is slightly better then the best result reported in the literature for this language.

A high-throughput dependency parser

2017

Dependency parsing is an important task in NLP, and it is used in many downstream tasks for analyzing the semantic structure of sentences. Analyzing very large corpora in a reasonable amount of time, however, requires a fast parser. In this thesis we develop a transitionbased dependency parser with a neural-network decision function which outperforms spaCy, Stanford CoreNLP, and MALTParser in terms of speed while having a comparable, and in some cases better, accuracy. We also develop several variations of our model to investigate the trade-off between accuracy and speed. This leads to a model with a greatly reduced feature set which is much faster but less accurate, as well as a more complex model involving a BiLSTM simultaneously trained to produce POS tags which is more accurate, but much slower. We compare the accuracy and speed of our different parser models against the three mentioned parsers on the Penn Treebank, Universal Dependencies English, and Ontonotes datasets using tw...

Improving parsing accuracy by combining diverse dependency parsers

Proceedings of the Ninth International Workshop on Parsing Technology - Parsing '05, 2005

This paper explores the possibilities of improving parsing results by combining outputs of several parsers. To some extent, we are porting the ideas of to the world of dependency structures. We differ from them in exploring context features more deeply. All our experiments were conducted on Czech but the method is language-independent. We were able to significantly improve over the best parsing result for the given setting, known so far. Moreover, our experiments show that even parsers far below the state of the art can contribute to the total improvement.

A pipeline model for bottom-up dependency parsing

2006

Abstract We present a new machine learning framework for multi-lingual dependency parsing. The framework uses a linear, pipeline based, bottom-up parsing algorithm, with a look ahead local search that serves to make the local predictions more robust. As shown, the performance of the first generation of this algorithm is promising.

Efficient Parsing of Syntactic and Semantic Dependency Stru ctures

2009

In this paper, we describe our system for the 2009 CoNLL shared task for joint parsing of syntactic and semantic dependency structures of multiple languages. Our system combines and implements efficient parsing techniques to get a high accuracy as well as very good parsing and training time. For the applications of syntactic and semantic parsing, the parsing time and memory footprint are very important. We think that also the development of systems can profit from this since one can perform more experiments in the given time. For the subtask of syntactic dependency parsing, we could reach the second place with an accuracy in average of 85.68 which is only 0.09 points behind the first ranked system. For this task, our system has the highest accuracy for English with 89.88, German with 87.48 and the out-of-domain data in average with 78.79. The semantic role labeler works not as well as our parser and we reached therefore the fourth place (ranked by the macro F1 score) in the joint task for syntactic and semantic dependency parsing.

Survey on parsing three dependency representations for English

In this paper we focus on practical issues of data representation for dependency parsing. We carry out an experimental comparison of (a) three syntactic dependency schemes; (b) three data-driven dependency parsers; and (c) the influence of two different approaches to lexical category disambiguation (aka tagging) prior to parsing. Comparing parsing accuracies in various setups, we study the interactions of these three aspects and analyze which configurations are easier to learn for a dependency parser.

A Root of a Problem: Optimizing Single-Root Dependency Parsing

2021

We describe two approaches to single-root dependency parsing that yield significant speed ups in such parsing. One approach has been previously used in dependency parsers in practice, but remains undocumented in the parsing literature, and is considered a heuristic. We show that this approach actually finds the optimal dependency tree. The second approach relies on simple reweighting of the inference graph being input to the dependency parser and has an optimal running time. Here, we again show that this approach is fully correct and identifies the highest-scoring parse tree. Our experiments demonstrate a manyfold speed up compared to a previous graph-based state-of-the-art parser without any loss in accuracy or optimality. 1