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1

Slivnik, Bostjan. "LL conflict resolution using the embedded left LR parser." Computer Science and Information Systems 9, no. 3 (2012): 1105–24. http://dx.doi.org/10.2298/csis111216023s.

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A method for resolving LL(k) conflicts using small LR(k) parsers (called embedded left LR(k) parsers) is described. An embedded left LR(k) parser is capable of (a) producing the prefix of the left parse of the input string and (b) stopping not on the end-of-file marker but on any string from the set of lookahead strings fixed at the parser generation time. The conditions regarding the termination of the embedded left LR(k) parser if used within LL(k) (and similar) parsers are defined and examined in-depth. It is proved that an LL(k) parser augmented with a set of embedded left LR(k) parsers ca
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Drozdov, Dmitry Sergeevich, and Stanislav Stanislavovich Mikhalkovich. "Development of Lightweight Parsers with Different Go Language Granularity." Russian Digital Libraries Journal 27, no. 6 (2024): 857–77. https://doi.org/10.26907/1562-5419-2024-27-6-857-877.

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We consider an approach to creating a family of lightweight grammars with the Any symbol denoting skipping code parts [1]. Definition and examples of increasing the granularity of grammar rules are given. Memory and time efficiency of lightweight parsers is analyzed on seven industrial repositories. It is shown that increasing grammar granularity does not significantly increase parser resource consumption and varies slightly depending on repository type and Go writing style. Furthermore, the advantages of using lightweight grammars with Any over full grammars are summarized. An example of usin
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Ballesteros, Miguel, Chris Dyer, Yoav Goldberg, and Noah A. Smith. "Greedy Transition-Based Dependency Parsing with Stack LSTMs." Computational Linguistics 43, no. 2 (2017): 311–47. http://dx.doi.org/10.1162/coli_a_00285.

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We introduce a greedy transition-based parser that learns to represent parser states using recurrent neural networks. Our primary innovation that enables us to do this efficiently is a new control structure for sequential neural networks—the stack long short-term memory unit (LSTM). Like the conventional stack data structures used in transition-based parsers, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. Our model captures three facets of the parser's state: (i) unbounded l
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Ryazanov, Yu D., and S. V. Nazina. "Building parsers based on syntax diagrams with multiport components." Prikladnaya Diskretnaya Matematika, no. 55 (2022): 102–19. http://dx.doi.org/10.17223/20710410/55/8.

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The problem of constructing parsers from syntax diagrams with multiport components (SD) is solved. An algorithm for constructing a parser based on the GLL algorithm is proposed, which results in the compact representation of the input chain parse forest. The proposed algorithm makes it possible to build parsers based on the SD of an arbitrary structure and does not require preliminary SD transformations. We introduce the concepts of “inference tree” and “parsing forest” for SD and describe the data structures used by the parser, such as a graph-structured stack, a parser descriptor, and a comp
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Morio, Gaku, Hiroaki Ozaki, Yuta Koreeda, Terufumi Morishita, and Toshinori Miyoshi. "i-Parser: Interactive Parser Development Kit for Natural Language Processing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 16091–93. http://dx.doi.org/10.1609/aaai.v35i18.18021.

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This demonstration paper presents i-Parser, a novel development kit that produces high-performance semantic parsers. i-Parser converts training graphs into sequences written in a context-free language, then our proposed model learns to generate the sequences. With interactive configuration and visualization, users can easily build their own parsers. Benchmark results of i-Parser showed high performances of various parsing tasks in natural language processing.
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Partridge, Andrew, and David Wright. "Predictive parser combinators need four values to report errors." Journal of Functional Programming 6, no. 2 (1996): 355–64. http://dx.doi.org/10.1017/s0956796800001714.

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AbstractA combinator-based parser is a parser constructed directly from a BNF grammar, using higher-order functions (combinators) to model the alternative and sequencing operations of BNF. This paper describes a method for constructing parser combinators that can be used to build efficient predictive parsers which accurately report the cause of parsing errors. The method uses parsers that return values (parse trees or error indications) decorated with one of four tags.
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Ho, Edward Kei Shin, and Lai Wan Chan. "How to Design a Connectionist Holistic Parser." Neural Computation 11, no. 8 (1999): 1995–2016. http://dx.doi.org/10.1162/089976699300016061.

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Connectionist holistic parsing offers a viable and attractive alternative to traditional algorithmic parsers. With exposure to a limited subset of grammatical sentences and their corresponding parse trees only, a holistic parser is capable of learning inductively the grammatical regularity underlying the training examples that affects the parsing process. In the past, various connectionist parsers have been proposed. Each approach had its own unique characteristics, and yet some techniques were shared in common. In this article, various dimensions underlying the design of a holistic parser are
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Huang, Yan, Akira Murakami, Theodora Alexopoulou, and Anna Korhonen. "Dependency parsing of learner English." International Journal of Corpus Linguistics 23, no. 1 (2018): 28–54. http://dx.doi.org/10.1075/ijcl.16080.hua.

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Abstract Current syntactic annotation of large-scale learner corpora mainly resorts to “standard parsers” trained on native language data. Understanding how these parsers perform on learner data is important for downstream research and application related to learner language. This study evaluates the performance of multiple standard probabilistic parsers on learner English. Our contributions are three-fold. Firstly, we demonstrate that the common practice of constructing a gold standard – by manually correcting the pre-annotation of a single parser – can introduce bias to parser evaluation. We
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Berger, Ulrich, Alison Jones, and Monika Seisenberger. "Program extraction applied to monadic parsing." Journal of Logic and Computation 29, no. 4 (2019): 487–518. http://dx.doi.org/10.1093/logcom/exv078.

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Abstract This article outlines a proof-theoretic approach to developing correct and terminating monadic parsers. Using modified realizability, we extract formally verified and terminating programs from formal proofs. By extracting both primitive parsers and parser combinators, it is ensured that all complex parsers built from these are also correct, complete and terminating for any input. We demonstrate the viability of our approach by means of two case studies: we extract (i) a small arithmetic calculator and (ii) a non-deterministic natural language parser. The work is being carried out in t
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Kravchenko, Daniil, Yury Kravchenko, Ali Mahmoud Mansour, Juman Mohammad та Nikolai Pavlov. "Алгоритм оптимизации извлечения ключевых слов на основе применения лингвистического парсера". Informatics and Automation 23, № 2 (2024): 467–94. http://dx.doi.org/10.15622/ia.23.2.6.

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This article presents an analytical comparison between constituency parsing and dependency parsing – two types of parsing used in the field of natural language processing (NLP). The study introduces an algorithm to enhance keyword extraction, employing the noun phrase extraction feature of the parser to filter out unsuitable phrases. This algorithm is implemented using three different parsers: Spacy, AllenNLP and Stazna. The effectiveness of this algorithm was compared with two popular methods (Yake, Rake) on a dataset of English texts. Experimental results show that the proposed algorithm wit
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Pitler, Emily. "A Crossing-Sensitive Third-Order Factorization for Dependency Parsing." Transactions of the Association for Computational Linguistics 2 (December 2014): 41–54. http://dx.doi.org/10.1162/tacl_a_00164.

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Parsers that parametrize over wider scopes are generally more accurate than edge-factored models. For graph-based non-projective parsers, wider factorizations have so far implied large increases in the computational complexity of the parsing problem. This paper introduces a “crossing-sensitive” generalization of a third-order factorization that trades off complexity in the model structure (i.e., scoring with features over multiple edges) with complexity in the output structure (i.e., producing crossing edges). Under this model, the optimal 1-Endpoint-Crossing tree can be found in O( n4) time,
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HUTTON, GRAHAM, and ERIK MEIJER. "Monadic parsing in Haskell." Journal of Functional Programming 8, no. 4 (1998): 437–44. http://dx.doi.org/10.1017/s0956796898003050.

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This paper is a tutorial on defining recursive descent parsers in Haskell. In the spirit of one-stop shopping, the paper combines material from three areas into a single source. The three areas are functional parsers (Burge, 1975; Wadler, 1985; Hutton, 1992; Fokker, 1995), the use of monads to structure functional programs (Wadler, 1990, 1992a, 1992b), and the use of special syntax for monadic programs in Haskell (Jones, 1995; Peterson et al., 1996). More specifically, the paper shows how to define monadic parsers using do notation in Haskell.Of course, recursive descent parsers defined by han
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Diatchki, Iavor S., Mike Dodds, Harrison Goldstein, et al. "Daedalus: Safer Document Parsing." Proceedings of the ACM on Programming Languages 8, PLDI (2024): 816–40. http://dx.doi.org/10.1145/3656410.

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Despite decades of contributions to the theoretical foundations of parsing and the many tools available to aid in parser development, many security attacks in the wild still exploit parsers. The issues are myriad—flaws in memory management in contexts lacking memory safety, flaws in syntactic or semantic validation of input, and misinterpretation of hundred-page-plus standards documents. It remains challenging to build and maintain parsers for common, mature data formats. In response to these challenges, we present Daedalus, a new domain-specific language (DSL) and toolchain for writing safe p
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Ferraro, Jeffrey, Ye Ye, Per Gesteland, et al. "The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance." Applied Clinical Informatics 08, no. 02 (2017): 560–80. http://dx.doi.org/10.4338/aci-2016-12-ra-0211.

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SummaryObjectives: This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems. Effectiveness is evaluated on how well NLP supports downstream influenza case-detection for disease surveillance.Methods: We independently developed two NLP parsers, one at Intermountain Healthcare (IH) in Utah and the other at University of Pittsburgh Medical Center (UPMC) using local clinical notes from emergency department (ED) encounters of influenza. We measured NLP parser per
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Sangati, Federico, and Frank Keller. "Incremental Tree Substitution Grammar for Parsing and Sentence Prediction." Transactions of the Association for Computational Linguistics 1 (December 2013): 111–24. http://dx.doi.org/10.1162/tacl_a_00214.

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In this paper, we present the first incremental parser for Tree Substitution Grammar (TSG). A TSG allows arbitrarily large syntactic fragments to be combined into complete trees; we show how constraints (including lexicalization) can be imposed on the shape of the TSG fragments to enable incremental processing. We propose an efficient Earley-based algorithm for incremental TSG parsing and report an F-score competitive with other incremental parsers. In addition to whole-sentence F-score, we also evaluate the partial trees that the parser constructs for sentence prefixes; partial trees play an
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BALLESTEROS, MIGUEL, and JOAKIM NIVRE. "MaltOptimizer: Fast and effective parser optimization." Natural Language Engineering 22, no. 2 (2014): 187–213. http://dx.doi.org/10.1017/s1351324914000035.

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AbstractStatistical parsers often require careful parameter tuning and feature selection. This is a nontrivial task for application developers who are not interested in parsing for its own sake, and it can be time-consuming even for experienced researchers. In this paper we present MaltOptimizer, a tool developed to automatically explore parameters and features for MaltParser, a transition-based dependency parsing system that can be used to train parser's given treebank data. MaltParser provides a wide range of parameters for optimization, including nine different parsing algorithms, an expres
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Rezwan, Ahmed. "Deconstruction of Rohinton Mistry's powerless fictional Parsees in contrast to the most powerful non-fictional Parsees of India: Gustad Noble, Dina Dalal and Nariman Vakeel as against Nusli Wadia, Parmeshwar Godrej and Ratan Tata." Deconstruction of Rohinton Mistry's powerless fictional Parsees in contrast to the most powerful non-fictional Parsees of India: Gustad Noble, Dina Dalal and Nariman Vakeel as against Nusli Wadia, Parmeshwar Godrej and Ratan Tata 3, no. 2 (2024): 109–15. https://doi.org/10.53413/IJTELL.2022.3115.

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This study delves into Rohinton Mistry'sParsee characters, such as Gustad Noble, DinaDalal, and Nariman Vakeel. Rather thandissecting the characters, the focus is onunderstanding their significance for the Parseecommunity in India, comparing them to reallife figures like Nusli Wadia, ParmeshwarGodrej, and Ratan Tata. Character breakdownsreveal stark differences, portraying Gustad as aweak Parsee facing cultural challenges,contrasting with Wadia's strength and businesssuccess. Similar distinctions are found in DinaDalal and Parmeshwar Godrej, highlightingthe struggles of a constrained Parsee wo
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KIPPS, JAMES R. "ADVANCED PARSER-GENERATION TECHNOLOGY FOR AI LANGUAGE APPLICATIONS." International Journal on Artificial Intelligence Tools 01, no. 04 (1992): 545–61. http://dx.doi.org/10.1142/s0218213092000053.

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A side effect of AI research is the development of programming languages as vehicles for experimentation and demonstration of concepts. These AI languages generally require some form of parser front end, which can be nontrivial to build. Parser generators can ease the task of language development, but commonly available generators use parsing technologies that severely constrain the level of syntactic sophistication, such as allowing at most one symbol of look ahead. Further, these generators are most often targeted for C and Ada applications; parser generators for LISP applications are not wi
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Eldridge, Elizabeth H., Elizabeth Trowbridge, Mui Van Zandt, Atif Adam, and Christina D. Mack. "Unstructured EMR data hold the key to oncology studies: Methods for validating NLP-extracted phenotypes from EMR notes." Journal of Clinical Oncology 42, no. 16_suppl (2024): 11133. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.11133.

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11133 Background: Natural language processing (NLP) parsers are advanced algorithms designed to parse values of interest from free-text stored in Electronic Medical Records (EMR). Given the importance of information stored in free-text for use in oncology research, it is crucial to understand how accurately NLP algorithms are extracting oncology-specific measures of interest. We focus on validating NLP-parsers that identify three measures critical to oncology research by evaluating how closely the NLP-parser results match manual chart abstractor review: AJCC summary cancer stage group (cancer
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Ali, Hassan, Muhammad Shumail Naveed, Dilawar Naseem, and Jawaid Shabbir. "LL (1) Parser versus GNF inducted LL (1) Parser on Arithmetic Expressions Grammar: A Comparative Study." Quaid-e-Awam University Research Journal of Engineering, Science & Technology 18, no. 02 (2020): 89–101. http://dx.doi.org/10.52584/qrj.1802.14.

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The prime objective of the proposed study is to determine the induction of Greibach Normal Form (GNF) in Arithmetic Expression Grammars to improve the processing speed of conventional LL(1) parser. Conventional arithmetic expression grammar and its equivalent LL(1) is used in the study which is converted. A transformation method is defined which converts the selected grammar into a Greibach normal form that is further converted into a GNF based parser through a method proposed in the study. These two parsers are analyzed by considering 399 cases of arithmetic expressions. During statistical an
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VANDEVENTER, ANNE. "Creating a grammar checker for CALL by constraint relaxation: a feasibility study." ReCALL 13, no. 1 (2001): 110–20. http://dx.doi.org/10.1017/s095834400100101x.

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Intelligent feedback on learners’ full written sentence productions requires the use of Natural Language Processing (NLP) tools and, in particular, of a diagnosis system. Most syntactic parsers, on which grammar checkers are based, are designed to parse grammatical sentences and/or native speaker productions. They are therefore not necessarily suitable for language learners. In this paper, we concentrate on the transformation of a French syntactic parser into a grammar checker geared towards intermediate to advanced learners of French. Several techniques are envisaged to allow the parser to ha
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Cao, Junjie, Zi Lin, Weiwei Sun, and Xiaojun Wan. "Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing." Computational Linguistics 47, no. 1 (2021): 43–68. http://dx.doi.org/10.1162/coli_a_00395.

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Abstract In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models. To reflect state-of-the-art neural NLP technologies, a factorization-based parser is introduced that can produce Elementary Dependency Structures much more accurately than previous data-driven parsers. We conduct a suite of tests for different linguistic phenomena to analyze the grammatical competence of different parsers, where we show that, despite comparable performance overall,
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Vadas, David, and James R. Curran. "Parsing Noun Phrases in the Penn Treebank." Computational Linguistics 37, no. 4 (2011): 753–809. http://dx.doi.org/10.1162/coli_a_00076.

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Noun phrases (nps) are a crucial part of natural language, and can have a very complex structure. However, this np structure is largely ignored by the statistical parsing field, as the most widely used corpus is not annotated with it. This lack of gold-standard data has restricted previous efforts to parse nps, making it impossible to perform the supervised experiments that have achieved high performance in so many Natural Language Processing (nlp) tasks. We comprehensively solve this problem by manually annotating np structure for the entire Wall Street Journal section of the Penn Treebank. T
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Handzhiyski, Nikolay, and Elena Somova. "Tunnel Parsing with the Token’s Lexeme." Cybernetics and Information Technologies 22, no. 2 (2022): 125–44. http://dx.doi.org/10.2478/cait-2022-0021.

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Abstract The article describes a string recognition approach, engraved in the parsers generated by Tunnel Grammar Studio that use the tunnel parsing algorithm, of how a lexer and a parser can operate on the input during its recognition. Proposed is an addition of the augmented Backus-Naur form syntax that enables the formal language to be expressed with a parser grammar and optionally with an additional lexer grammar. The tokens outputted from the lexer are matched to the phrases in the parser grammar by their name and optionally by their lexeme, case sensitively or insensitively.
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Guo, Yinuo, Zeqi Lin, Jian-Guang Lou, and Dongmei Zhang. "Iterative Utterance Segmentation for Neural Semantic Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12937–45. http://dx.doi.org/10.1609/aaai.v35i14.17530.

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Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To address this issue, we present a novel framework for boosting neural semantic parsers via iterative utterance segmentation. Given an input utterance, our framework iterates between two neural modules: a segmenter for segmenting a span from the utterance, and a parser for mapping the span into a partial meaning representation. Then, these intermediate parsing results are composed into the final meaning representation.
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Nishida, Noriki, and Hideki Nakayama. "Unsupervised Discourse Constituency Parsing Using Viterbi EM." Transactions of the Association for Computational Linguistics 8 (July 2020): 215–30. http://dx.doi.org/10.1162/tacl_a_00312.

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In this paper, we introduce an unsupervised discourse constituency parsing algorithm. We use Viterbi EM with a margin-based criterion to train a span-based discourse parser in an unsupervised manner. We also propose initialization methods for Viterbi training of discourse constituents based on our prior knowledge of text structures. Experimental results demonstrate that our unsupervised parser achieves comparable or even superior performance to fully supervised parsers. We also investigate discourse constituents that are learned by our method.
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Merkys, Andrius, Antanas Vaitkus, Justas Butkus, Mykolas Okulič-Kazarinas, Visvaldas Kairys, and Saulius Gražulis. "COD::CIF::Parser: an error-correcting CIF parser for the Perl language." Journal of Applied Crystallography 49, no. 1 (2016): 292–301. http://dx.doi.org/10.1107/s1600576715022396.

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A syntax-correcting CIF parser,COD::CIF::Parser, is presented that can parse CIF 1.1 files and accurately report the position and the nature of the discovered syntactic problems. In addition, the parser is able to automatically fix the most common and the most obvious syntactic deficiencies of the input files. Bindings for Perl, C and Python programming environments are available. Based onCOD::CIF::Parser, thecod-toolspackage for manipulating the CIFs in the Crystallography Open Database (COD) has been developed. Thecod-toolspackage has been successfully used for continuous updates of the data
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Chaudhary, Pavan Kurariya, Prashant, Jahnavi Bodhankar, Lenali Singh, and Ajai Kumar. "Interlingual Syntactic Parsing: An Optimized Head-Driven Parsing for English to Indian Language Machine Translation." International Journal on Natural Language Computing 13, no. 4 (2024): 21–31. http://dx.doi.org/10.5121/ijnlc.2024.13402.

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In the era of Artificial Intelligence (AI), significant progress has been made by enabling machines to understand and communicate in human languages. Central to this progress are parsers, which play a vital role in syntactic analysis and support various Natural language Processing (NLP) applications, including Machine Translation and sentiment analysis. This paper introduces a robust implementation of an optimized Head-Driven Parser designed to advance NLP capabilities beyond the limitations of traditional Lexicalized Tree Adjoining Grammar (L-TAG) based Parser. Traditional parser, while effec
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Ballesteros, Miguel, and Joakim Nivre. "Going to the Roots of Dependency Parsing." Computational Linguistics 39, no. 1 (2013): 5–13. http://dx.doi.org/10.1162/coli_a_00132.

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Dependency trees used in syntactic parsing often include a root node representing a dummy word prefixed or suffixed to the sentence, a device that is generally considered a mere technical convenience and is tacitly assumed to have no impact on empirical results. We demonstrate that this assumption is false and that the accuracy of data-driven dependency parsers can in fact be sensitive to the existence and placement of the dummy root node. In particular, we show that a greedy, left-to-right, arc-eager transition-based parser consistently performs worse when the dummy root node is placed at the
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Kiperwasser, Eliyahu, and Yoav Goldberg. "Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations." Transactions of the Association for Computational Linguistics 4 (December 2016): 313–27. http://dx.doi.org/10.1162/tacl_a_00101.

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We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very sim
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Gamallo Otero, Pablo, and Isaac González López. "A grammatical formalism based on patterns of Part of Speech tags." International Journal of Corpus Linguistics 16, no. 1 (2011): 45–71. http://dx.doi.org/10.1075/ijcl.16.1.03gam.

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In this paper, we describe a grammatical formalism, called DepPattern, to write dependency grammars using patterns of Part of Speech (PoS) tags augmented with lexical and morphological information. The formalism inherits ideas from Sinclair’s work and Pattern Grammar. To properly analyze semi-fixed idiomatic expressions, DepPattern distinguishes between open-choice and idiomatic rules. A grammar is defined as a set of lexical-syntactic rules at different levels of abstraction. In addition, a compiler was implemented so as to generate deterministic and robust parsers from DepPattern grammars. T
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Berant, Jonathan, and Percy Liang. "Imitation Learning of Agenda-based Semantic Parsers." Transactions of the Association for Computational Linguistics 3 (December 2015): 545–58. http://dx.doi.org/10.1162/tacl_a_00157.

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Semantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning and agenda-based parsing to train a semantic parser that searches partial logical forms in a more strategic order. Empirically, our parser reduces the number of constructed partial logical forms by an order of magnitude, and obtains a 6x-9x speedup over fixed-order parsing, while maintaining comparable accuracy.
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Goldberg, Yoav, Francesco Sartorio, and Giorgio Satta. "A Tabular Method for Dynamic Oracles in Transition-Based Parsing." Transactions of the Association for Computational Linguistics 2 (December 2014): 119–30. http://dx.doi.org/10.1162/tacl_a_00170.

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We develop parsing oracles for two transition-based dependency parsers, including the arc-standard parser, solving a problem that was left open in (Goldberg and Nivre, 2013). We experimentally show that using these oracles during training yields superior parsing accuracies on many languages.
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BAARS, ARTHUR I., ANDRES LÖH, and S. DOAITSE SWIERSTRA. "FUNCTIONAL PEARL Parsing permutation phrases." Journal of Functional Programming 14, no. 6 (2004): 635–46. http://dx.doi.org/10.1017/s0956796804005143.

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A permutation phrase is a sequence of elements (possibly of different types) in which each element occurs exactly once and the order is irrelevant. Some of the permutable elements may be optional. We show how to extend a parser combinator library with support for parsing such free-order constructs. A user of the library can easily write parsers for permutation phrases and does not need to care about checking and reordering the recognized elements. Applications include the generation of parsers for attributes of XML tags and Haskell's record syntax.
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Ortin, Francisco, Jose Quiroga, Oscar Rodriguez-Prieto, and Miguel Garcia. "An empirical evaluation of Lex/Yacc and ANTLR parser generation tools." PLOS ONE 17, no. 3 (2022): e0264326. http://dx.doi.org/10.1371/journal.pone.0264326.

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Parsers are used in different software development scenarios such as compiler construction, data format processing, machine-level translation, and natural language processing. Due to the widespread usage of parsers, there exist different tools aimed at automizing their generation. Two of the most common parser generation tools are the classic Lex/Yacc and ANTLR. Even though ANTLR provides more advanced features, Lex/Yacc is still the preferred choice in many university courses. There exist different qualitative comparisons of the features provided by both approaches, but no study evaluates emp
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Mashreghi-Moghadam, Parisa, Tarek Ould-Bachir, and Yvon Savaria. "PrismParser: A Framework for Implementing Efficient P4-Programmable Packet Parsers on FPGA." Future Internet 16, no. 9 (2024): 307. http://dx.doi.org/10.3390/fi16090307.

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The increasing complexity of modern networks and their evolving needs demand flexible, high-performance packet processing solutions. The P4 language excels in specifying packet processing in software-defined networks (SDNs). Field-programmable gate arrays (FPGAs) are ideal for P4-based packet parsers due to their reconfigurability and ability to handle data transmitted at high speed. This paper introduces three FPGA-based P4-programmable packet parsing architectural designs that translate P4 specifications into adaptable hardware implementations called base, overlay, and pipeline, each optimiz
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Rahman, Arief, and Ayu Purwarianti. "Dense Word Representation Utilization in Indonesian Dependency Parsing." Jurnal Linguistik Komputasional (JLK) 3, no. 1 (2020): 12. http://dx.doi.org/10.26418/jlk.v3i1.33.

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Available Indonesian dependency parsers can be considered worse than other languages’ parsers that have been researched thoroughly. Currently, Indonesia dependency parsers can’t reliably parse sentences with gerund(s) and/or ellipsis correctly. This is because of the sparse feature representation that causes difficulty in parsing these types of sentences. In this research, dense representation is proposed for Indonesian dependency parser. The use of dense word representation may allow better generalization and gives more information regarding the words to be parsed, which allows a more accurat
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38

Strübbe, Simon M., Alexander T. D. Grünwald, Irina Sidorenko, and Renée Lampe. "A Rule-Based Parser in Comparison with Statistical Neuronal Approaches in Terms of Grammar Competence." Applied Sciences 15, no. 1 (2024): 87. https://doi.org/10.3390/app15010087.

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The “Easy Language” standard was created to help individuals with cognitive disabilities understand texts more easily. Typically, text simplification is performed by language experts and is available for limited materials. We introduce a new software tool designed to analyze and simplify any text according to the “Easy Language” rules. This tool uses a rule-based system, conducting a full grammatical analysis of each sentence and then simplifying it into a grammatically correct form. Unlike neuronal approaches, which are based on statistics and are very popular today, our rule-based approach e
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Nivre, Joakim, Yoav Goldberg, and Ryan McDonald. "Constrained Arc-Eager Dependency Parsing." Computational Linguistics 40, no. 2 (2014): 249–527. http://dx.doi.org/10.1162/coli_a_00184.

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Arc-eager dependency parsers process sentences in a single left-to-right pass over the input and have linear time complexity with greedy decoding or beam search. We show how such parsers can be constrained to respect two different types of conditions on the output dependency graph: span constraints, which require certain spans to correspond to subtrees of the graph, and arc constraints, which require certain arcs to be present in the graph. The constraints are incorporated into the arc-eager transition system as a set of preconditions for each transition and preserve the linear time complexity
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Jia, Xiaodong, Ashish Kumar, and Gang Tan. "A derivative-based parser generator for visibly Pushdown grammars." Proceedings of the ACM on Programming Languages 5, OOPSLA (2021): 1–24. http://dx.doi.org/10.1145/3485528.

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In this paper, we present a derivative-based, functional recognizer and parser generator for visibly pushdown grammars. The generated parser accepts ambiguous grammars and produces a parse forest containing all valid parse trees for an input string in linear time. Each parse tree in the forest can then be extracted also in linear time. Besides the parser generator, to allow more flexible forms of the visibly pushdown grammars, we also present a translator that converts a tagged CFG to a visibly pushdown grammar in a sound way, and the parse trees of the tagged CFG are further produced by runni
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Liu, Zoey, and Emily Prud’hommeaux. "Data-driven Parsing Evaluation for Child-Parent Interactions." Transactions of the Association for Computational Linguistics 11 (2023): 1734–53. http://dx.doi.org/10.1162/tacl_a_00624.

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Abstract We present a syntactic dependency treebank for naturalistic child and child-directed spoken English. Our annotations largely follow the guidelines of the Universal Dependencies project (UD [Zeman et al., 2022]), with detailed extensions to lexical and syntactic structures unique to spontaneous spoken language, as opposed to written texts or prepared speech. Compared to existing UD-style spoken treebanks and other dependency corpora of child-parent interactions specifically, our dataset is much larger (44,744 utterances; 233,907 words) and contains data from 10 children covering a wide
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Passos, Leonardo, Mariza Andrade Silva Bigonha, and Roberto Bigonha. "An LALR Parser Generator Supporting Conflict Resolution." JUCS - Journal of Universal Computer Science 14, no. (21) (2008): 3447–64. https://doi.org/10.3217/jucs-014-21-3447.

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Despite all the advance brought by LALR parsing method by DeRemer in the late 60's, conflicts continue to be removed in a non-productive way, by means of analysis of a huge amount of textual and low level data dumped by the parser generator tool. For the purpose of changing this scenario, we present a parser generator capable of automatically removing some types of conflicts, along with a supported methodology that guides the process of manual removal. We also discuss the internal algorithms and how the created parsers are compact in terms of memory usage.
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Ni, Ansong, Pengcheng Yin, and Graham Neubig. "Merging Weak and Active Supervision for Semantic Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8536–43. http://dx.doi.org/10.1609/aaai.v34i05.6375.

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A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-desired results. The fully-supervised training of such parser requires NL/MR pairs, annotated by domain experts, which makes them expensive to collect. However, weakly-supervised semantic parsers are learnt only from pairs of NL and expected execution results, leaving the MRs latent. While weak supervision is cheaper to acquire, learning from this input poses difficulties. It demands that parsers search a large space w
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Yuan, Yunzhe, Yong Jiang, and Kewei Tu. "Bidirectional Transition-Based Dependency Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7434–41. http://dx.doi.org/10.1609/aaai.v33i01.33017434.

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Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner. During this process, an early prediction error may negatively impact the prediction of subsequent actions. In this paper, we propose a simple framework for bidirectional transitionbased parsing. During training, we learn a left-to-right parser and a right-to-left parser separately. To parse a sentence, we perform joint decoding with the two parsers. We pro
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Hill, Steve. "Combinators for parsing expressions." Journal of Functional Programming 6, no. 3 (1996): 445–64. http://dx.doi.org/10.1017/s0956796800001799.

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AbstractThis paper describes a scheme for constructing parsers based on the top-down combinator approach. In particular, it describes a set of combinators for parsing expressions described by ambiguous grammars with precedence and associativity rules. The new combinators embody the mechanical grammar manipulations typically employed to remove left-recursion and hence help to avoid the possibility of a non-terminating parser. A number of approaches to the problem are described—the most elegant and efficient method is based on continuation passing. As a practical demonstration, a parser for the
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Loganathan, Ramasamy, Mareček David, and Žabokrtský Zdenčk. "Multilingual Dependency Parsing: Using Machine Translated Texts Instead of Parallel Corpora." Prague Bulletin of Mathematical Linguistics 102, no. 1 (2014): 93–104. http://dx.doi.org/10.2478/pralin-2014-0017.

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Abstract This paper revisits the projection-based approach to dependency grammar induction task. Traditional cross-lingual dependency induction tasks one way or the other, depend on the existence of bitexts or target language tools such as part-of-speech (POS) taggers to obtain reasonable parsing accuracy. In this paper, we transfer dependency parsers using only approximate resources, i.e., machine translated bitexts instead of manually created bitexts. We do this by obtaining the the source side of the text from a machine translation (MT) system and then apply transfer approaches to induce pa
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Hernault, Hugo, Helmut Prendinger, David A. du Verle, and Mitsuru Ishizuka. "HILDA: A Discourse Parser Using Support Vector Machine Classification." Dialogue & Discourse 1, no. 3 (2010): 1–33. http://dx.doi.org/10.5087/dad.2010.003.

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Discourse structures have a central role in several computational tasks, such as question-answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classification. SVM classifiers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear
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48

Kiperwasser, Eliyahu, and Yoav Goldberg. "Easy-First Dependency Parsing with Hierarchical Tree LSTMs." Transactions of the Association for Computational Linguistics 4 (December 2016): 445–61. http://dx.doi.org/10.1162/tacl_a_00110.

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We suggest a compositional vector representation of parse trees that relies on a recursive combination of recurrent-neural network encoders. To demonstrate its effectiveness, we use the representation as the backbone of a greedy, bottom-up dependency parser, achieving very strong accuracies for English and Chinese, without relying on external word embeddings. The parser’s implementation is available for download at the first author’s webpage.
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Sanders, Alton, and Ruth Sanders. "Designing and Implementing a Syntactic Parser." CALICO Journal 5, no. 1 (2013): 77–86. http://dx.doi.org/10.1558/cj.v5i1.77-86.

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Large computer parsers, or language analyzers, can be designed to recognize most correct structures of a natural language and reject most incorrect structures. In order for these parsers to function as effective writing aids for language students, several other kinds of programs must be designed to work with them. A lexicon database, programs to aid the developers, and a word processor for eventual student users are examples. Further commercial tools such as the programming language and editors should be chosen for their ability to enhance teanmwork among language and computer specialists. Exa
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SRINIVAS, BANGALORE. "A lightweight dependency analyzer for partial parsing." Natural Language Engineering 6, no. 2 (2000): 113–38. http://dx.doi.org/10.1017/s1351324900002345.

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In this paper, we present a novel approach to partial parsing that produces dependency links between words of a sentence. The partial parser called a lightweight dependency analyzer uses information encoded in supertags and hence can produce constituency-based as well as dependency-based analyses. The lightweight dependency analyzer has been used for text chunking, including noun and verb group chunking. We also present a proposal for a general framework for parser evaluation that is applicable for evaluating both constituency-based and dependency-based, partial and complete parsers. The perfo
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