Sentence Compression by Removing Recursive Structure from Parse Tree

نویسندگان

  • Seiji Egawa
  • Yoshihide Kato
  • Shigeki Matsubara
چکیده

Sentence compression is a task of generating a grammatical short sentence from an original sentence, retaining the most important information. The existing methods of removing the constituents in the parse tree of an original sentence cannot deal with recursive structures which appear in the parse tree. This paper proposes a method to remove such structure and generate a grammatical short sentence. Compression experiments have shown the method to provide an ability to sentence compression comparable to the existing methods and generate good compressed sentences for sentences including recursive structures, which the previous methods failed to compress.

برای دانلود رایگان 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

Trimming CFG Parse Trees for Sentence Compression Using Machine Learning Approaches

Sentence compression is a task of creating a short grammatical sentence by removing extraneous words or phrases from an original sentence while preserving its meaning. Existing methods learn statistics on trimming context-free grammar (CFG) rules. However, these methods sometimes eliminate the original meaning by incorrectly removing important parts of sentences, because trimming probabilities ...

متن کامل

Answer Extraction by Recursive Parse Tree Descent

We develop a recursive neural network (RNN) to extract answers to arbitrary natural language questions from supporting sentences, by training on a crowdsourced data set (to be released upon presentation). The RNN defines feature representations at every node of the parse trees of questions and supporting sentences, when applied recursively, starting with token vectors from a neural probabilisti...

متن کامل

Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees

In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summarization. We propose an innovative sentence compression method by considering every node in the constituent parse tree and deciding its status – remove or retain. Integer liner programming with discriminative training is used to solve the problem. Under this model, we incorporate vario...

متن کامل

Modelling Sentence Pairs with Tree-structured Attentive Encoder

We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs. Since existing attentive models exert attention on the sequential structure, we propose a way to incorporate attention into the tree topology. Specially, given a pair of sentences, our attentive encoder uses the representation of one sen...

متن کامل

Bidirectional Recursive Neural Networks for Token-Level Labeling with Structure

Recently, deep architectures, such as recurrent and recursive neural networks have been successfully applied to various natural language processing tasks. Inspired by bidirectional recurrent neural networks which use representations that summarize the past and future around an instance, we propose a novel architecture that aims to capture the structural information around an input, and use it t...

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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008