Efficient Third-Order Dependency Parsers

نویسندگان

  • Terry Koo
  • Michael Collins
چکیده

We present algorithms for higher-order dependency parsing that are “third-order” in the sense that they can evaluate substructures containing three dependencies, and “efficient” in the sense that they require only O(n4) time. Importantly, our new parsers can utilize both sibling-style and grandchild-style interactions. We evaluate our parsers on the Penn Treebank and Prague Dependency Treebank, achieving unlabeled attachment scores of 93.04% and 87.38%, respectively.

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

Efficient Inner-to-outer Greedy Algorithm for Higher-order Labeled Dependency Parsing

Many NLP systems use dependency parsers as critical components. Jonit learning parsers usually achieve better parsing accuracies than two-stage methods. However, classical joint parsing algorithms significantly increase computational complexity, which makes joint learning impractical. In this paper, we proposed an efficient dependency parsing algorithm that is capable of capturing multiple edge...

متن کامل

High-order Graph-based Neural Dependency Parsing

In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. Th...

متن کامل

Fourth-Order Dependency Parsing

We present and implement a fourth-order projective dependency parsing algorithm that effectively utilizes both “grand-sibling” style and “tri-sibling” style interactions of third-order and “grand-tri-sibling” style interactions of forth-order factored parts for performance enhancement. This algorithm requires O(n5) time and O(n4) space. We implement and evaluate the parser on two languages—Engl...

متن کامل

Title of Thesis: Learning Structured Classifiers for Statistical Dependency Parsing Learning Structured Classifiers for Statistical Dependency Parsing

In this thesis, I present three supervised and one semi-supervised machine learning approach for improving statistical natural language dependency parsing. I first introduce a generative approach that uses a strictly lexicalised parsing model where all the parameters are based on words, without using any part-of-speech (POS) tags or grammatical categories. Then I present an improved large margi...

متن کامل

Online Large-Margin Training of Dependency Parsers

We present an effective training algorithm for linearly-scored dependency parsers that implements online largemargin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a competitive dependency accuracy for both English and Czech with no language specific enhancements.

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:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010