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About 1,734 papers for “syntax trees”

Syntax Trees

Victor N. Kasyanov, Viadimir. A. Evstigneev · 2000

Cited by 0

Abstract Syntax Trees for SMILES

Richard L. Apodaca · Front Matter · 2020

SMILES is the de facto standard for information exchange in cheminformatics. But despite this language's prominent role, low-level tools and documentation remain sparse and incomplete. Recent articles here have highlighted the development of a SMILES grammar and a parser implementation in Rust. This article extends that theme by introducing another powerful tool for the analysis and manipulation of SMILES using Rust.

Cited by 0Open access

Abstract Syntax Trees for SMILES

Richard L. Apodaca · Front Matter · 2020

SMILES is the de facto standard for information exchange in cheminformatics. But despite this language's prominent role, low-level tools and documentation remain sparse and incomplete. Recent articles here have highlighted the development of a SMILES grammar and a parser implementation in Rust. This article extends that theme by introducing another powerful tool for the analysis and manipulation of SMILES using Rust.

Cited by 0Open access

Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees

Jiangang Bai, Yujing Wang, Yiren Chen et al. · Greater South Information System · 2021

Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.Meanwhile, syntactic information has been proved to be crucial for the success of NLP applications.However, how to incorporate the syntax trees effectively and efficiently into pre-trained Transformers is still unsettled.In this paper, we address this problem by proposing a novel framework named Syntax-BERT.This framework works in a plug-and-play mode and is applicable to an arbitrary pre-trained checkpoint based on Transformer architecture.Experiments on various datasets of natural language understanding verify the effectiveness of syntax trees and achieve consistent improvement over multiple pre-trained models, including BERT, RoBERTa, and T5.

Cited by 18Open access

Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees

Jiangang Bai, Yujing Wang, Yiren Chen et al. · arXiv (Cornell University) · 2021

Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information has been proved to be crucial for the success of NLP applications. However, how to incorporate the syntax trees effectively and efficiently into pre-trained Transformers is still unsettled. In this paper, we address this problem by proposing a novel framework named Syntax-BERT. This framework works in a plug-and-play mode and is applicable to an arbitrary pre-trained checkpoint based on Transformer architecture. Experiments on various datasets of natural language understanding verify the effectiveness of syntax trees and achieve consistent improvement over multiple pre-trained models, including BERT, RoBERTa, and T5.

Cited by 4Open access

Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees

Jiangang Bai, Yujing Wang, Yiren Chen et al. · 2021

Jiangang Bai, Yujing Wang, Yiren Chen, Yaming Yang, Jing Bai, Jing Yu, Yunhai Tong. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Cited by 51Open access

Editing Syntax Trees on the Surface

Peter Ljunglöf · Chalmers Publication Library (Chalmers University of Technology) · 2011

Proceedings of the 18th Nordic Conference of Computational Linguistics
\nNODALIDA 2011.
\nEditors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa.
\nNEALT Proceedings Series, Vol. 11 (2011), 138-145.
\n© 2011 The editors and contributors.
\nPublished by
\nNorthern European Association for Language
\nTechnology (NEALT)
\nhttp://omilia.uio.no/nealt .
\nElectronically published at
\nTartu University Library (Estonia)
\nhttp://hdl.handle.net/10062/16955 .

Cited by 8Open access

fAST: Flattening Abstract Syntax Trees for Efficiency

Yijun Yu · 2019

Frequently source code analysis tools need to exchange internal representations of abstract syntax trees (AST) with each other. Conveniently, and intuitively, the externalised representations are in the form of hierarchical trees. We argue, counter-intuitively, that hierarchical representation is not the most efficient way for source analysis tools to exchange parsed AST. In this work, we propose to speed up AST parsing whilst preserving the equivalence of hierarchies in binary forms: (1) AST could be saved as a flat one-dimensional array where pointers to tree nodes are converted into integer offsets, and (2) such flattened AST are more efficient to access by programming tools through the generated application programming interfaces (API). In programming language-agnostic evaluations, we show that parsing flattened AST becomes 100x faster than in textual form AST on a benchmark of open-source projects of 6 different programming languages.

Cited by 7

Diffusion On Syntax Trees For Program Synthesis

Shreyas Kapur, Erik Jenner, Stuart D. Russell · arXiv (Cornell University) · 2024

Large language models generate code one token at a time. Their autoregressive generation process lacks the feedback of observing the program's output. Training LLMs to suggest edits directly can be challenging due to the scarcity of rich edit data. To address these problems, we propose neural diffusion models that operate on syntax trees of any context-free grammar. Similar to image diffusion models, our method also inverts ``noise'' applied to syntax trees. Rather than generating code sequentially, we iteratively edit it while preserving syntactic validity, which makes it easy to combine this neural model with search. We apply our approach to inverse graphics tasks, where our model learns to convert images into programs that produce those images. Combined with search, our model is able to write graphics programs, see the execution result, and debug them to meet the required specifications. We additionally show how our system can write graphics programs for hand-drawn sketches.

Cited by 0Open access

Compressed Abstract Syntax Trees for Mobile Code

Christian H. Storck, et al. · 2009

Abstract syntax trees (ASTs) have numerous advantages as a mobile code format over the more commonly used bytecode-based formats. Not only are ASTs portable, inherently safer, and more suitable for optimization, but we show that they also compress more densely. We have developed a prototype framework for AST compression, which we have used to compress Java programs. Our generic implementation reduces the size of regular Java archives by a factor of 3 to 8 and improves on the best published Java-specific compression scheme by 5–50%.

Cited by 0

Augmenting abstract syntax trees for program understanding

Chris Welty · 2002

Program understanding efforts by individual maintainers are dominated by a process known as discovery, which is characterized by low-level searches through the source code and documentation to obtain information that is important to the maintenance task. Discovery is complicated by the delocalization of information in the source code, and can consume from 40-60% of a maintainer's time. This paper presents an ontology for representing code-level knowledge based on abstract syntax trees, that was developed in the context of studying maintenance problems in a small software company. The ontology enables the utilization of automated reasoning to counter delocalization, and thus to speed up discovery.

Cited by 35

Compressed Abstract Syntax Trees for Mobile Code

2008

Abstract syntax trees (ASTs) have numerous advantages as a mobile code format over the more commonly used bytecode-based formats. Not only are ASTs portable, inherently safer, and more suitable for optimization, but we show that they also compress more densely. We have developed a prototype framework for AST compression, which we have used to compress Java programs. Our generic implementation reduces the size of regular Java archives by a factor of 3 to 8 and improves on the best published Java-specific compression scheme by 5–50%.

Cited by 5

Clone Detection Using Abstract Syntax Suffix Trees

RAimar Falke, et al. · 2006

Reusing software through copying and pasting is a continuous plague in software development despite the fact that it creates serious maintenance problems. Various techniques have been proposed to find duplicated redundant code (also known as software clones). A recent study has compared these techniques and shown that token-based clone detection based on suffix trees is extremely fast but yields clone candidates that are often no syntactic units [26]. Current techniques based on abstract syntax trees—on the other hand—find syntactic clones but are considerably less efficient. This paper describes how we can make use of suffix trees to find clones in abstract syntax trees. This new approach is able to find syntactic clones in linear time and space. The paper reports the results of several large case studies in which we empirically compare the new technique to other techniques using the Bellon benchmark for clone detectors.

Cited by 0

Sentence syntax trees should be made from morphemes. Semantically ordered trees

Dinar Qurbanov · CogPrints (Cogprints) · 2015

Some critique of usage of sentence parse trees in modern linguistics. Two propositions on constructing trees, as mentioned in the title. Introduction of an English-to-Tatar translator program that is being developed by the author. Precedence by specificity.

Cited by 0Open access

Investigation of transformations between abstract syntax trees of a concrete programming language and language-independent abstract syntax trees.

Stankevičius, Rytis, · KTUePubl (Repository of Kaunas University of Technology) · 2010

Tools for transforming tree-like data structures are used for solving a range of different problems, such as creation of compilers, code optimizers, documentation and code generation tools, etc. But none of these existing tools seem to be specialized in transforming abstract syntax trees to a language-independent format or from it. This master‘s thesis describes a system that takes concrete abstract syntax trees (ASTs that represent the structure of code written in a real programming language, such a C#, Java, Ruby, etc.) and transforms them into language-independent abstract syntax trees, and vice versa. Rules for mapping one AST to the other are defined in XML-based configuration files that have a simple and clear structure. The system can be used as a stand-alone tool or used as component in third party software. Both the system’s core libraries and tools are written in Java. The purpose of the system’s investigation was to prove two things: that the system can correctly transform a given specific AST to an equivalent generic AST, and vise versa; and that the transformation execution time is within an acceptable range. Both goals were met. This led to a conclusion that the system is a suitable candidate for use by third party tools that handle code migration, refactoring, model transformation and other types of similar tasks.

Cited by 0Open access