Efficient Stacked Dependency Parsing by Forest Reranking

نویسندگان

  • Katsuhiko Hayashi
  • Shuhei Kondo
  • Yuji Matsumoto
چکیده

This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. To improve efficiency and accuracy, this paper also proposes a novel shift-reduce parser that eliminates the spurious ambiguity of arcstandard transition systems. Testing on the English Penn Treebank data, forest reranking gave a state-of-the-art unlabeled dependency accuracy of 93.12.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Third-order Variational Reranking on Packed-Shared Dependency Forests

We propose a novel forest reranking algorithm for discriminative dependency parsing based on a variant of Eisner’s generative model. In our framework, we define two kinds of generative model for reranking. One is learned from training data offline and the other from a forest generated by a baseline parser on the fly. The final prediction in the reranking stage is performed using linear interpol...

متن کامل

A Search in the Forest: Efficient Algorithms for Parsing and Machine Translation based on Packed Forests A DISSERTATION PROPOSAL in Computer and Information Science

Many problems in Natural Language Processing (NLP) involves an efficient search for the best derivation over (exponentially) many candidates. For example, a parser aims to find the best syntactic tree for a given sentence among all derivations under a grammar, and a machine translation (MT) decoder explores the space of all possible translations of the source-language sentence. In these cases, ...

متن کامل

Statistical Ltag Parsing

STATISTICAL LTAG PARSING Libin Shen Aravind K. Joshi In this work, we apply statistical learning algorithms to Lexicalized Tree Adjoining Grammar (LTAG) parsing, as an effort toward statistical analysis over deep structures. LTAG parsing is a well known hard problem. Statistical methods successfully applied to LTAG parsing could also be used in many other structure prediction problems in NLP. F...

متن کامل

A Reranking Approach for Dependency Parsing with Variable-sized Subtree Features

Employing higher-order subtree structures in graph-based dependency parsing has shown substantial improvement over the accuracy, however suffers from the inefficiency increasing with the order of subtrees. We present a new reranking approach for dependency parsing that can utilize complex subtree representation by applying efficient subtree selection heuristics. We demonstrate the effectiveness...

متن کامل

Combine Constituent and Dependency Parsing via Reranking

This paper presents a reranking approach to combining constituent and dependency parsing, aimed at improving parsing performance on both sides. Most previous combination methods rely on complicated joint decoding to integrate graphand transition-based dependency models. Instead, our approach makes use of a high-performance probabilistic context free grammar (PCFG) model to output k-best candida...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • TACL

دوره 1  شماره 

صفحات  -

تاریخ انتشار 2013