Software support for the construction of syntactic pattern recognisers by a research engineer

Syntactic pattern recognition is introduced and it is suggested that a research engineer will probably be less familiar with the language theory underlying syntactic pattern recognition than with the statistical ideas connected with decision theoretic methods. For this reason application of syntactic pattern recognition will only develop if software support is provided. The steps of grammatical inference, recogniser construction and recogniser optimisation are outlined. They are included in a scheme by which, working interactively and iteratively, a syntactic pattern recogniser can be produced. Some comments on an implementation are given.     

A non-left-to-right,non-directional parser for ambiguous lattices

In syntactic pattern recognition the need of reducing computational power may require the use of more general parsers than the standard ones, allowing to efficiently exploit the structural knowledge for limiting the extraction of primitive patterns. In this paper a new flexible, non-directional, non-left-to-right parsing scheme is presented; it has worst case space complexity O(N²) and time complexity O(N³), where N is the length of the input string. These upper bounds are lowered for particular classes of context-free grammars as, for instance, linear or non-ambiguous ones.     

Conversion and parsing of tree transducers for syntactic pattern analysis

The conversion algorithms for two classes of tree transducers, i.e., simple generalized syntax-directed tree translation (SGSDTT) and generalized finite state transformation (GFST), are proposed. A top-down backtrack parsing algorithm for a GFST is presented. The minimum distance structure-preserved error-correcting tree automaton is also extended to be a parser for SGSDTT. Finally, the tree transducers are applied to modeling and analysis of human motion.This work was supported by the NSF Grant IST 79-18884.     

Design pattern recovery through visual language parsing and source code analysis

In this paper we propose an approach for recovering structural design patterns from object-oriented source code. The recovery process is organized in two phases. In the first phase, the design pattern instances are identified at a coarse-grained level by considering the design structure only and exploiting a parsing technique used for visual language recognition. Then, the identified candidate patterns are validated by a fine-grained source code analysis phase. The recognition process is supported by a tool, namely design pattern recovery environment, which allowed us to assess the retrieval effectiveness of the proposed approach on six public-domain programs and libraries.     

基于句式结构的汉语图解析句法设计

梳理了汉语语法学界对“句式”这一术语的认识分歧;从中文信息处理角度分析了当前本领域句法分析和树库构建缺乏句式结构的现状;对黎氏语法形式化研究作了一个最新的综述,指出其在句式结构方面的优势和仍存在的不足;以黎氏语法图解法为原型改造设计出一种新型的汉语图解析句法,具体包括图形化的句法结构表示和结构化的XML存储格式。     

Structural pattern recognition,homomorphisms, and arrangements

This paper discusses the general supervised pattern discrimination problem from a structural point of view. We show that both the problem of determining a decision rule and the problem of applying a decision rule are problems of finding homomorphisms, whether the pattern data structure is an N-tuple as in statistical pattern recognition, or a string or its generalizations as in syntactic pattern recognition. We then introduce the concept of an arrangement, which is a labeled N-ary relation, as a more complex pattern data structure and show how decision rules can be constructed and applied to arrangements using the homomorphism concept. The methodology suggested in the paper provides a structural pattern recognition generalization to phrase-structured syntactic pattern recognition.     

Serializing C intermediate representations for efficient and portable parsing

C static analysis tools often use intermediate representations (IRs) that organize program data in a simple, well‐structured manner. However, the C parsers that create IRs are slow, and because they are difficult to write, only a few implementations exist, limiting the languages in which a C static analysis can be written. To solve these problems, we investigate two language‐independent, on‐disk representations of C IRs: one using XML and the other using an Internet standard binary encoding called eXternal Data Representation (XDR). We benchmark the parsing speeds of both options, finding the XML to be about a factor of 2 slower than parsing C and the XDR over 6 times faster. Furthermore, we show that the XML files are far too large at 19 times the size of C source code, whereas XDR is only 2.2 times the C size. We also demonstrate the portability of our XDR system by presenting a C source code querying tool in Ruby. Our solution and the insights we gained from building it will be useful to analysis authors and other clients of C IRs. We have made our software freely available for download at http://www.cs.umd.edu/projects/PL/scil/ . Copyright © 2010 John Wiley&Sons, Ltd.     

An LR parsing technique for extended context-free grammars

Extended context-free grammars allow regular expressions to appear in productions right hand sides, and are a clear and natural way to describe the syntax of programming languages.In this paper an LR parsing technique for extended context-free grammars is presented, which is based on an underlying transformation of the grammar into an equivalent context-free one.The technique is suitable for inclusion in one-pass compilers: the implementation requires little extensions to the algorithms working for normal LR grammars. Besides describing the parsing method, the paper shows also the algorithms for deriving the parsing tables; tables optimization is also discussed. Finally, this technique is compared with other proposals appeared in the literature.     

A syntactic method for image segmentation and object recognition

In this paper, image segmentation and object recognition using syntactic methods are investigated. The segmentation process is embedded in the parsing algorithm. The approach can be described by a syntax-directed relaxation process. Previous error-correcting parsing algorithms, however, have not tackled the segmentation problem satisfactorily. Experimental results using Synthetic Aperture Radar (SAR) images are given.     

A synthesis of type-checking and parsing

The context-free grammars and parsers conventionally used to define and interpret concrete syntax lead to constrained expression notations. This paper gives algorithms which support less constrained, more intuitive notations. These algorithms can be viewed as context-sensitive parsers, where the context that is taken into account is the type structure of the expression. Or they can be viewed as type checkers that determine the form of the abstract syntax. The approach taken seamlessly integrates parsing and type-checking in the formalism of Markov algorithms. The interpretation of concrete syntax by these algorithms is unambiguous. At least one algorithm, DMA-T, is practical; its worst case time is 0(tn²) where n is the length of the expression and t is the maximum length of any type expression.     

Efficient regular grammatical inference for pattern recognition

A new and simple method for the inference of regular grammars or finite automata is presented. It is based on the analysis of the successive appearances of the terminal symbols in the learning strings. It is shown that for syntactic pattern recognition applications, this method is more efficient than other algorithms already proposed.     

Software visualization of LR parsing and synthesized attribute evaluation

Visual YACC is a tool that automatically creates visualizations of the YACC LR parsing process and synthesized attribute computation. The Visual YACC tool works by instrumenting a standard YACC grammar with graphics calls that draw the appropriate data structures given the current actions by the parser. The new grammar is processed by the YACC tools and the resulting parser displays the parse stack and parse tree for every step of the parsing process of a given input string. Visual YACC was initially designed to be used in compiler construction courses to supplement the teaching of parsing and syntax directed evaluation. We have also found it to be useful in the difficult task of debugging YACC grammars. In this paper, we describe this tool and how it is used in both contexts. We also detail two different implementations of this tool: one that produces a parser written in C with calls to Motif; and a second implementation that generates Java source code. Copyright © 1999 John Wiley & Sons, Ltd.     

Interactive learning of node selecting tree transducer

We develop new algorithms for learning monadic node selection queries in unranked trees from annotated examples, and apply them to visually interactive Web information extraction. We propose to represent monadic queries by bottom-up deterministic Node Selecting Tree Transducers (NSTTs), a particular class of tree automata that we introduce. We prove that deterministic NSTTs capture the class of queries definable in monadic second order logic (MSO) in trees, which Gottlob and Koch (2002) argue to have the right expressiveness for Web information extraction, and prove that monadic queries defined by NSTTs can be answered efficiently. We present a new polynomial time algorithm in RPNI-style that learns monadic queries defined by deterministic NSTTs from completely annotated examples, where all selected nodes are distinguished. In practice, users prefer to provide partial annotations. We propose to account for partial annotations by intelligent tree pruning heuristics. We introduce pruning NSTTs—a formalism that shares many advantages of NSTTs. This leads us to an interactive learning algorithm for monadic queries defined by pruning NSTTs, which satisfies a new formal active learning model in the style of Angluin (1987). We have implemented our interactive learning algorithm integrated it into a visually interactive Web information extraction system—called SQUIRREL—by plugging it into the Mozilla Web browser. Experiments on realistic Web documents confirm excellent quality with very few user interactions during wrapper induction. Editor: Georgios Paliouras and Yasubumi Sakakibara     

基于汉语组块的产品特征观点对提取与情感分析研究

为了解决用户评论文本中的产品特征-观点对的提取及情感分析问题,本文利用组块分析提取产品特征,从中寻找到频繁项集,并用PMI对候选产品特征进行过滤,得到产品的特征集合;利用特征与情感词在位置上的邻接关系,提取情感词并组成特征-观点对,通过SO-PMI方法进行情感倾向分析。为验证该方法的有效性,以酒店评论文本为例,从中提取酒店的特征-观点对并进行情感分析,准确率为76.68%,召回率为70.84%。实验结果表明引入组块分析可以有效的解决商品评论的细粒度情感分类问题。     

VHDL高级综合前词法语法分析的研究与实现

利用YACC与LEX来完成VHDL语言高级综合前端的词法与语法分析器设计。介绍词法分析模块，语法分析模块以及错误处理模块的设计方法。利用Windows平台卞的Parser Generator 2．0生成词法与语法的C文件，在Visual C＋＋6．0中编译该C文件，生成可执行程序。     

An algorithm for the inference of tree grammars

An algorithm for the inference of tree grammars from sample trees is presented. The procedure, which is based on the properties of self-embedding and regularity, produces a reduced tree grammar capable of generating all the samples used in the inference process as well as other trees similar in structure. The characteristics of the algorithm are illustrated by experimental results.Work supported by the Office of Naval Research, Arlington, Virginia, under contract N00014-71-A-0121-0005.     

结合依存句法分析与交互注意力机制的隐式方面提取

隐式方面提取对于提升细粒度情感分析的准确性具有重要意义,然而现有隐式方面提取技术在处理大规模数据时泛化能力不强。为此,提出结合依存句法分析与交互注意力机制的隐式方面提取模型。首先利用预训练语言模型BERT生成文本的初始表征,然后传递给依存句法引导的自注意力层再次处理,再将两次处理的结果经交互注意力机制进一步提取特征,最终用分类器判断句子所属的隐式方面类别。与基线BERT及其他深度神经网络模型对比,所提模型在增强的SemEval隐式方面数据集上取得了更高的F₁与AUC值,证明了模型的有效性。     

Theory and practice of unparsed patterns for metacompilation

Several software development tools support the matching of concrete syntax user-supplied patterns against the application source code, allowing the detection of invalid, risky, inefficient or forbidden constructs. When applied to compilers, this approach is called metacompilation. These patterns are traditionally parsed into tree patterns, i.e., fragments of abstract-syntax trees with metavariables, which are then matched against the abstract-syntax tree corresponding to the parsing of the source code. Parsing the patterns requires extending the grammar of the application programming language with metavariables, which can be difficult, especially in the case of legacy tools. Instead, we propose a novel matching algorithm which is independent of the programming language because the patterns are not parsed and, as such, are called unparsed patterns. It is as efficient as the classic pattern matching while being easier to implement. By giving up the possibility of static checks that parsed patterns usually enable, it can be integrated within any existing utility based on abstract-syntax trees at a low cost. We present an in-depth coverage of the practical and theoretical aspects of this new technique by describing a working minimal patch for the GNU C compiler, together with a small standalone prototype punned Matchbox, and by lying out a complete formalisation, including mathematical proofs of key algorithmic properties, like correctness and equivalence to the classic matching.