Quantifying syntax similarity with a polynomial representation of dependency trees

نویسندگان

چکیده

We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and measure based on the representation quantify syntax similarity. The encodes accurate comprehensive information about structure relations of words in sentence, which enables in-depth analysis trees with data tools. apply polynomial-based methods analyze sentences ParallelUniversal Dependencies treebanks. Specifically, we compare their translations different languages, perform syntactic typology study available languages Parallel Universal also demonstrate discuss potential measuring diversity corpora.

برای دانلود باید عضویت طلایی داشته باشید

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

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Dependency Structure Trees in Syntax Based Machine Translation

Machine Translation (MT) has long been an unsolved problem and more so an interesting and engaging problem. MT deals with the translation of a sentence in a source language into a sentence in the target language preserving the meaning in its full detail. This requires the computer to encode the knowledge of both languages in a representation that can be used at runtime to translate a give input...

متن کامل

Structured Lexical Similarity via Convolution Kernels on Dependency Trees

A central topic in natural language processing is the design of lexical and syntactic features suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical similarities. We define efficient and powerful kernels for measuring the similarity between dependency structures, whose surf...

متن کامل

Polynomial Representation for the Expected Length of Minimal Spanning Trees∗

In this paper, we investigate the polynomial integrand of an integral formula that yields the expected length of the minimal spanning tree of a graph whose edges are uniformly distributed over the interval [0, 1]. In particular, we derive a general formula for the coefficients of the polynomial and use it to express the first few coefficients in terms of the structure of the underlying graph; e...

متن کامل

Capturing Dependency Syntax with "Deep" Sequential Models

Neural network (“deep learning”) models are taking over machine learning approaches for language by storm. In particular, recurrent neural networks (RNNs), which are flexible non-markovian models of sequential data, were shown to be effective for a variety of language processing tasks. Somewhat surprisingly, these seemingly purely sequential models are very capable at modeling syntactic phenome...

متن کامل

A Statistical Theory of Dependency Syntax

A generative statistical model of dependency syntax is proposed based on Tesni ere's classical theory. It provides a stochastic formalization of the original model of syntactic structure and augments it with a model of the string realization process, the latter which is lacking in Tesni ere's original work. The resulting theory models crossing dependency links, discontinuous nuclei and string m...

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

ژورنال

عنوان ژورنال: Glottometrics (Lüdenscheid. Print)

سال: 2022

ISSN: ['1617-8351', '2625-8226']

DOI: https://doi.org/10.53482/2022_53_402