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The purpose of this paper is to re-examine the relationship between grammars and processing systems in light of the various forms of experimental research (especially of an electrophysiological nature) that has been conducted in the last fifteen years or so. First, the notion of 'processing strategy' or 'heuristics processing' is considered followed by a discussion of structures of great morphosyntactic complexity that parsing systems seem to tackle by simply respecting complex grammatical laws, instead of by resorting to shortcuts. Then, grammatical illusions and what these can teach us about the processing of grammar are considered. It is argued that illusions allow us to discern a few explanatory principles that may redefine the way we see parser–grammar relations. Among these is the idea that how long illusions last in the online-to-offline transition depends in part on their 'templatability', that is, the ease with which they become gestaltic templates. Another key idea is that some apparent illusions are in fact nothing more than grammar contemplated at work as in slow motion. [ABSTRACT FROM AUTHOR] more...

This is an industrial experience report on a large semi‐automated migration of legacy test code in C and C++. The particular migration was enabled by automating most of the maintenance steps. Without automation this particular large‐scale migration would not have been conducted, due to the risks involved in manual maintenance (risk of introducing errors, risk of unexpected rework, and loss of productivity). We describe and evaluate the method of automation we used on this real‐world case. The benefits were that by automating analysis, we could make sure that we understand all the relevant details for the envisioned maintenance, without having to manually read and check our theories. Furthermore, by automating transformations we could reiterate and improve over complex and large scale source code updates, until they were "just right." The drawbacks were that, first, we have had to learn new metaprogramming skills. Second, our automation scripts are not readily reusable for other contexts; they were necessarily developed for this ad‐hoc maintenance task. Our analysis shows that automated software maintenance as compared to the (hypothetical) manual alternative method seems to be better both in terms of avoiding mistakes and avoiding rework because of such mistakes. It seems that necessary and beneficial source code maintenance need not to be avoided, if software engineers are enabled to create bespoke (and ad‐hoc) analysis and transformation tools to support it. [ABSTRACT FROM AUTHOR] more...

The syntax of the programming languages has a significant impact on the definition and validation of mathematical calculations. In particular, the management of code identification and validation processes can be made easier and faster, depending on the parametric behavior of the functions. In this article, a programming language that supports the use of mathematical function structures is designed and an interpreter, which can evaluate the source code written in this language, is developed. The language syntax is represented by an LL (k) grammar defined in the BNF notation. The interpreter consists of several basic components such as parser, semantic controller and code evaluator, each of which makes a different kind of code interpretation. The LL (k) parser component used for the syntactic analysis of the language is generated via an automatic code generation tool called JavaCC. The other components work on the abstract syntactic tree that this parser generates. To illustrate the use of the language with code samples, several mathematical algorithms that include calculations on different sequences of numbers, are programmed and interpreted. more...

Natural language processing problems (such as speech recognition, text-based data mining, and text or speech generation) are becoming increasingly important. Before effectively approaching many of these problems, it is necessary to process the syntactic structures of the sentences. Syntactic parsing is the task of constructing a syntactic parse tree over a sentence which describes the structure of the sentence. Parse trees are used as part of many language processing applications. In this paper, we present a multi-lingual dependency parser. Using advanced deep learning techniques, our parser architecture tackles common issues with parsing such as long-distance head attachment, while using `architecture engineering' to adapt to each target language in order to reduce the feature engineering often required for parsing tasks. We implement a parser based on this architecture to utilize transfer learning techniques to address important issues related with limited-resourced language. We exceed the accuracy of state-of-the-art parsers on languages with limited training resources by a considerable margin. We present promising results for solving core problems in natural language parsing, while also performing at state-of-the-art accuracy on general parsing tasks. more...

Sayısal hesaplama ya da diğer adıyla nümerik analiz, uygulamalı matematiğin önemli bir dalıdır. Matematiğinanalitik çözüm üretemediği veya üretilen çözümün uygulama açısından yüksek hesaplama karmaşıklığına sahipolduğu durumlarda sayısal yöntemler kullanılır. Bu makalede, sayısal yöntemlerin programlanabilmesinisağlayan bir programlama dilinin tasarımı yapılmış ve bu dilde yazılan kaynak kodun değerlendirmesiniyapabilen bir yorumlayıcı geliştirilmiştir. Dilin sözdizimi BNF (Backus Naur Form) notasyonunda tanımlananbir LL(k) grameri ile temsil edilmiştir. Yorumlayıcı, her biri kodun farklı türden yorumlamasını yapan ayrıştırıcı,anlamsal denetleyici, simgesel türev alıcı ve kod değerlendirici gibi birkaç temel bileşenden oluşmaktadır. Dilinsözdizim analizi için kullanılan LL(k) ayrıştırıcı bileşeni, otomatik kod üretim aracı olan JavaCC yardımıylaüretilmiştir. Diğer bileşenler bu ayrıştırıcının oluşturduğu soyut sözdizim ağacı üzerinde çalışmaktadır. Dilinkullanımına yönelik olarak, birkaç sayısal kök bulma yönteminin programlaması ve yorumlaması gösterilmiştir.Bazı popüler diller ile dil metriklerine dayalı bir karşılaştırma yapılmış ve koşum zamanları değerlendirilmiştir. more...

The use of teaching tools has become an alternative to complement the learning of school content. This paper provides some overview aspects of Compilation and performance analysis of the computational tools GALS, Grammophone, The Context Grammar Free Checker, Verto, and Parsing Simulator that were developed to support the compilation process and aim at assisting the learning in Compilers course. There are several known tools, but only a few were built for academic purposes and will be presented in this paper, as they were tested by students in the Compilers course in the Undergraduate course and also int the Master's course in Computer Science at a Brazilian Public University in Paraná to analyze hypotheses, to help verifying parsing examples and to exchange experiences about these Compiler tools. It was observed that the lexical and mainly syntactic analysis phases become more didactic and attractive to the students, making it easier to understand their functionalities and implementation of a compiler as a whole. GALS has shown to be a good option with a simple interface, working with lexical and syntactic analysis for more than one language (Java, C++ and Delphi). Studies of Context Free Grammars in LL(1), LR(0) and LR(1) format may be favored not only with GALS, but also with the tools Grammophone and The Context Grammar Free Checker. Verto, on the other hand, works didactically, not only on the lexical and syntactic analysis steps (the latter also with LR(1) Parser), but also on code generation. Parsing Simulator proved to be an intuitive tool and also presents an extensive collection of syntactic analysis options showing the step by step LL(1) and LR(K) analysis tables, promoting teaching-learning in Compilers. [ABSTRACT FROM AUTHOR] more...

The syntax of the programming languages has a significant impact on the definition and validation of mathematical calculations. In particular, the management of code identification and validation processes can be made easier and faster, depending on the parametric behavior of the functions. In this article, a programming language that supports the use of mathematical function structures is designed and an interpreter, which can evaluate the source code written in this language, is developed. The language syntax is represented by an LL (k) grammar defined in the BNF notation. The interpreter consists of several basic components such as parser, semantic controller and code evaluator, each of which makes a different kind of code interpretation. The LL (k) parser component used for the syntactic analysis of the language is generated via an automatic code generation tool called JavaCC. The other components work on the abstract syntactic tree that this parser generates. To illustrate the use of the language with code samples, several mathematical algorithms that include calculations on different sequences of numbers, are programmed and interpreted. The paper also performs a comparative analysis of the language with some related ones. The paper also performs a comparative analysis of the language with some related ones based on some design principles and mathematical aspects. [ABSTRACT FROM AUTHOR] more...

Lately, discourse structure has received considerable attention due to the benefits its application offers in several NLP tasks such as opinion mining, summarization, question answering, text simplification, among others. When automatically analyzing texts, discourse parsers typically perform two different tasks: i) identification of basic discourse units (text segmentation) ii) linking discourse units by means of discourse relations, building structures such as trees or graphs. The resulting discourse structures are, in general terms, accurate at intra-sentence discourse-level relations, however they fail to capture the correct inter-sentence relations. Detecting the main discourse unit (the Central Unit) is helpful for discourse analyzers (and also for manual annotation) in improving their results in rhetorical labeling. Bearing this in mind, we set out to build the first two steps of a discourse parser following a top-down strategy: i) to find discourse units, ii) to detect the Central Unit. The final step, i.e. assigning rhetorical relations, remains to be worked on in the immediate future. In accordance with this strategy, our paper presents a tool consisting of a discourse segmenter and an automatic Central Unit detector. [ABSTRACT FROM AUTHOR] more...

The complexity of hardware designs is still increasing according to Moore’s law. With embedded systems being more and more intertwined and working together not only with each other, but also with their environments as cyber physical systems (CPSs), more streamlined development workflows are employed to handle the increasing complexity during a system’s design phase. SystemC is a C++ library for the design of hardware/software systems, enabling the designer to quickly prototype, e.g., a distributed CPS without having to decide about particular implementation details (such as whether to implement a feature in hardware or in software) early in the design process. Thereby, this approach reduces the initial implementation’s complexity by offering an abstract layer with which to build a working prototype. However, as SystemC is based on C++, analyzing designs becomes a difficult task due to the complex language features that are available to the designer. Several fundamentally different approaches for analyzing SystemC designs have been suggested. This work illustrates several different SystemC analysis approaches, including their specific advantages and shortcomings, allowing designers to pick the right tools to assist them with a specific problem during the design of a system using SystemC. more...

The composition of the scientific workforce shapes the direction of scientific research, directly through the selection of questions to investigate, and indirectly through its influence on the training of future scientists. In most fields, however, complete census information is difficult to obtain, complicating efforts to study workforce dynamics and the effects of policy. This is particularly true in computer science, which lacks a single, all-encompassing directory or professional organization. A full census of computer science would serve many purposes, not the least of which is a better understanding of the trends and causes of unequal representation in computing. Previous academic census efforts have relied on narrow or biased samples, or on professional society membership rolls. A full census can be constructed directly from online departmental faculty directories, but doing so by hand is expensive and time-consuming. Here, we introduce a topical web crawler for automating the collection of faculty information from web-based department rosters, and demonstrate the resulting system on the 205 PhD-granting computer science departments in the U.S. and Canada. This method can quickly construct a complete census of the field, and achieve over 99% precision and recall. We conclude by comparing the resulting 2017 census to a hand-curated 2011 census to quantify turnover and retention in computer science, in general and for female faculty in particular, demonstrating the types of analysis made possible by automated census construction. [ABSTRACT FROM AUTHOR] more...

In a self-paced reading study on German sluicing, Paape (Paape, 2016) found that reading times were shorter at the ellipsis site when the antecedent was a temporarily ambiguous garden-path structure. As a post-hoc explanation of this finding, Paape assumed that the antecedent’s memory representation was reactivated during syntactic reanalysis, making it easier to retrieve. In two eye tracking experiments, we subjected the reactivation hypothesis to further empirical scrutiny. Experiment 1, carried out in French, showed no evidence in favor in the reactivation hypothesis. Instead, results for one out of the three types of garden-path sentences that were tested suggest that subjects sometimes failed to resolve the temporary ambiguity in the antecedent clause, and subsequently failed to resolve the ellipsis. The results of Experiment 2, a conceptual replication of Paape’s (Paape, 2016) original study carried out in German, are compatible with the reactivation hypothesis, but leave open the possibility that the observed speedup for ambiguous antecedents may be due to occasional retrievals of an incorrect structure. [ABSTRACT FROM AUTHOR] more...

Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representations have been developed, such as the continuous bag of words (CBOW) and skip-gram models, and they have been extensively employed in a variety of NLP tasks. Because of the complex structure generated by the longer text lengths, such as sentences, algorithms appropriate for learning short textual representations are not applicable for learning long textual representations. One method of learning long textual representations is the Long Short-Term Memory (LSTM) network, which is suitable for processing sequences. However, the standard LSTM does not adequately address the primary sentence structure (subject, predicate and object), which is an important factor for producing appropriate sentence representations. To resolve this issue, this paper proposes the dependency-based LSTM model (D-LSTM). The D-LSTM divides a sentence representation into two parts: a basic component and a supporting component. The D-LSTM uses a pre-trained dependency parser to obtain the primary sentence information and generate supporting components, and it also uses a standard LSTM model to generate the basic sentence components. A weight factor that can adjust the ratio of the basic and supporting components in a sentence is introduced to generate the sentence representation. Compared with the representation learned by the standard LSTM, the sentence representation learned by the D-LSTM contains a greater amount of useful information. The experimental results show that the D-LSTM is superior to the standard LSTM for sentences involving compositional knowledge (SICK) data. [ABSTRACT FROM AUTHOR] more...

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, IEPA, and LLL) were used for evaluating the performance of our system. Experimental results show that our system achieves better f-score with five different corpora compared to other state-of-the-art systems. [ABSTRACT FROM AUTHOR] more...

In this paper, a platform is presented, that given a Stochastic Context-Free Grammar (SCFG), automatically outputs the description of a parser in synthesizable Hardware Description Language (HDL) which can be downloaded in an FPGA (Field Programmable Gate Arrays) board. Although the proposed methodology can be used for various inexact models, the probabilistic model is analyzed in detail and the extension to other inexact schemes is described. Context-Free Grammars (CFG) are augmented with attributes which represent the probability values. Initially, a methodology is proposed based on the fact that the probabilities can be evaluated concurrently with the parsing during the parse table construction by extending the fundamental parsing operation proposed by Chiang & Fu. Using this extended operation, an efficient architecture is presented based on Earley's parallel algorithm, which given an input string, generates the parse table while evaluating concurrently the probabilities of the generated dotted grammar rules in the table. Based on this architecture, a platform has been implemented that automatically generates the hardware design of the parser given a SCFG. The platform is suitable for embedded systems applications where a natural language interface is required or in pattern recognition tasks. The proposed hardware platform has been tested for various SCFGs and was compared with previously presented hardware parser for SCFGs based on Earley's parallel algorithm. The hardware generated by the proposed platform is much less complicated than the one of comparison and succeeds a speed-up of one order of magnitude. [ABSTRACT FROM AUTHOR] more...

This study addresses the question of whether native Mandarin Chinese speakers process and comprehend subject-extracted relative clauses (SRC) more readily than object-extracted relative clauses (ORC) in Mandarin Chinese. Presently, this has been a hotly debated issue, with various studies producing contrasting results. Using two eye-tracking experiments with ambiguous and unambiguous RCs, this study shows that both ORCs and SRCs have different processing requirements depending on the locus and time course during reading. The results reveal that ORC reading was possibly facilitated by linear/temporal integration and canonicity. On the other hand, similarity-based interference made ORCs more difficult, and expectation-based processing was more prominent for unambiguous ORCs. Overall, RC processing in Mandarin should not be broken down to a single ORC (dis)advantage, but understood as multiple interdependent factors influencing whether ORCs are either more difficult or easier to parse depending on the task and context at hand. [ABSTRACT FROM AUTHOR] more...

Objectives: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. Methods: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients’ diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. Results: Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution’s cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task. Conclusion: We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser. [ABSTRACT FROM AUTHOR] more...

Source code plagiarism in students solutions of programming tasks is a serious problem, especially important in distance learning. Naturally, it should be prevented, but publicly available code plagiarism detection tools are not fully adjusted to this purpose. This paper proposes the specific approach to detecting code duplicates. This approach is based on adapting of detection process to characteristics of programming tasks and comprise of freshly developed detecting tools, which could be configured and tuned to fit individual features of the programming task. Particular attention is paid to the possibility of an automatic elimination of duplicate codes from the set of all solutions. As a minimum, this requires the rejection of false-positive duplicates, even for simple, schematic tasks. The case in the use of tools is presented in this context. The discussion is illustrated by applying of proposed tools to duplicates detection in the set of actual, real-life, codes written in Java programming language. more...

Past research has produced evidence that parsing commitments strengthen over the processing of additional linguistic elements that are consistent with the commitments and undoing strong commitments takes more time than undoing weak commitments. It remains unclear, however, whether this so-called digging-in effect is exclusively due to the length of an ambiguous region or at least partly to the extra cost of processing these additional phrases. The current study addressed this issue by testing Japanese relative clause structure, where lexical content and sentence meaning were controlled for. The results showed evidence for a digging-in effect reflecting the strengthened commitment to an incorrect analysis caused by the processing of additional adjuncts. Our study provides strong support for the dynamical, self-organizing models of sentence processing but poses a problem for other models including serial two-stage models as well as frequency-based probabilistic models such as the surprisal theory. [ABSTRACT FROM AUTHOR] more...

In this paper a platform is presented, that given a stochastic context-free grammar (SCFG), automatically outputs the description of the parser in synthesizable hardware description language (HDL) which can be downloaded in an Field Programmable Gate Arrays (FPGA) board. Initially, according to our methodology the SCFG is augmented with attributes which store the probability values and can be evaluated through corresponding stack actions. The architecture of the produced system is based on a proposed extension of Earley's parallel algorithm, which given an input string, generates the parse trees in the form of an AND-Or parse tree. This AND-or parse tree is then traversed using a proposed tree traversal technique in order to execute the corresponding actions in the correct order, so as to compute the necessary probabilities. The platform is suitable for embedded systems applications where a natural language interface is required or in pattern recognition tasks. The parser generated by the presented platform has been tested for various SCFGs and compared to software approaches. The performance comparison is one to two orders of magnitude in favor of the presented hardware, compared to previous software approaches, depending on the application, the input string length and the number of produced trees. [ABSTRACT FROM AUTHOR] more...

The complexity of hardware designs is still increasing according to Moore's law. With embedded systems being more and more intertwined and working together not only with each other, but also with their environments as cyber physical systems (CPSs), more streamlined development workflows are employed to handle the increasing complexity during a system's design phase. SystemC is a C++ library for the design of hardware/software systems, enabling the designer to quickly prototype, e.g., a distributed CPS without having to decide about particular implementation details (such as whether to implement a feature in hardware or in software) early in the design process. Thereby, this approach reduces the initial implementation's complexity by offering an abstract layer with which to build a working prototype. However, as SystemC is based on C++, analyzing designs becomes a difficult task due to the complex language features that are available to the designer. Several fundamentally different approaches for analyzing SystemC designs have been suggested. This work illustrates several different SystemC analysis approaches, including their specific advantages and shortcomings, allowing designers to pick the right tools to assist them with a specific problem during the design of a system using SystemC. [ABSTRACT FROM AUTHOR] more...

Abstract: This paper demonstrates a self-checking (self-validating) spreadsheet. This checking analyzes the meaning or semantics of the spreadsheet's variables and equations using a parsing scheme. These semantics go beyond dimension, unit, and type checking to include the physical and mathematical formulae that dominate science, engineering, and mathematics. The spreadsheet is a client, JavaScript web application working with a server application. Entries in the spreadsheet are analyzed by semantic parsers on the server, and the representation and recognition of spreadsheet semantics are detailed. The intent of this prototype is to reduce the errors—errors in meaning—that commonly occur when spreadsheets are used. A prototype has been available on the Internet since early 2010 at http://semantics.grc.nasa.gov/cgi-bin/spread.cgi. [Copyright &y& Elsevier] more...

Abstract: We measured the extent to which information surrounding a base noun phrase reflects the presence of a gene name, and evaluated seven different parsers in their ability to provide information for that purpose. Using the GENETAG corpus as a gold standard, we performed machine learning to recognize from its context when a base noun phrase contained a gene name. Starting with the best lexical features, we assessed the gain of adding dependency or dependency-like relations from a full sentence parse. Features derived from parsers improved performance in this partial gene mention recognition task by a small but statistically significant amount. There were virtually no differences between parsers in these experiments. [Copyright &y& Elsevier] more...

Grammarware comprises grammars and all grammar-dependent software. The term grammar is meant here in the sense of all established grammar formalisms and grammar notations including context-free grammars, class dictionaries, and XML schemas as well as some forms of tree and graph grammars. The term grammar-dependent software refers to all software that involves grammar knowledge in an essential manner. Archetypal examples of grammar-dependent software are parsers, program converters, and XML document processors. Despite the pervasive role of grammars in software systems, the engineering aspects of grammarware are insufficiently understood. We lay out an agenda that is meant to promote research on increasing the productivity of grammarware development and on improving the quality of grammarware. To this end, we identify the problems with the current grammarware practices, the barriers that currently hamper research, and the promises of an engineering discipline for grammarware, its principles, and the research challenges that have to be addressed. [ABSTRACT FROM AUTHOR] more...

The wide variety of remote sensors used in Intelligent Transportation Systems (ITS) applications (loops, probe vehicles, radar, cameras, etc.) has created a need for general methods by which data can be shared among agencies and users who own disparate computer systems. In this paper, we present a methodology that demonstrates that it is possible to create, encode, and decode a self-describing data stream using: 1) existing data description language standards; 2) parsers to enforce language compliance; 3) a simple content language that flows out of the data description language; and 4) architecture neutral encoders and decoders based on ASN.1. [ABSTRACT FROM PUBLISHER] more...

An important function of an agent is to be “on the lookout” for bits of information that are interesting to its user, even if these items appear in the midst of a larger body of unstructured information. But how to tell these agents which patterns are meaningful and what to do with the result? Especially when agents are used to recognize text, they are usually driven by parsers which require input in the form of textual grammar rules. Editing grammars is difficult and error-prone for end users. Grammex [“Grammars by Example”] is the first direct manipulation interface designed to allow non-expert users to define grammars interactively. The user presents concrete examples of text that he or she would like the agent to recognize. Rules are constructed by an iterative process, where Grammex heuristically parses the example, displays a set of hypotheses, and the user critiques the system's suggestions. Actions to take upon recognition are also demonstrated by example. [ABSTRACT FROM AUTHOR] more...