scholarly journals LL conflict resolution using the embedded left LR parser

2012 ◽  
Vol 9 (3) ◽  
pp. 1105-1124 ◽  
Author(s):  
Bostjan Slivnik
Keyword(s):  
Conflict Resolution ◽  
Generation Time ◽  
Input String ◽  
Context Free Grammar ◽  
Parser Generation ◽  
Context Free ◽  
Asymptotic Time

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 can parse any deterministic context-free grammar in the same asymptotic time as LR(k) parser. As the embedded left LR(k) parser produces the prefix of the left parse, the LL(k) parser augmented with embedded left LR(k) parsers still produces the left parse and the compiler writer does not need to bother with different parsing strategies during the compiler implementation.

scholarly journals Description Of Context Free Grammars In Json Format For Parser Generators

2020 ◽  
Vol 23 (6) ◽  
pp. 1301-1323
Author(s):  
Oleg Konstantinovich Osipov
Keyword(s):  
Data Format ◽  
Context Free Grammar ◽  
Parser Generation ◽  
Context Free ◽  
Parser Generator ◽  
Context Free Grammars

Analysis of various presentations for context free grammars provided with parser generators. A new description format of context free grammars is proposed. Given a representation of context free grammar in JSON format. The concept of a new parser generator based on JSON data format of describing context free grammars is presented. Described a parser generation scheme based on that concept.

Translating imperative algorithms into declarative, functional terms

2017 ◽  
Author(s):  
C. M. Sperberg-McQueen
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Input String ◽  
General Context ◽  
Functional Language ◽  
Context Free Grammar ◽  
Defining Relations ◽  
Small Set ◽  
Procedural Description ◽  
Context Free

In building up subroutine libraries for XSLT and XQuery, it is sometimes useful to re-implement standard algorithms in the new language. Such re-implementation can be challenging, because standard algorithms are often described in imperative terms; before being reimplemented in XSLT or XQuery, the algorithm must first be re-understood in a declarative and functional way. Some of the challenges which arise in this process can be illustrated by the example of Earley parsing. Earley’s algorithm can parse an input string against any context-free grammar in Backus-Naur Form. Unlike recursive-descent or table-driven LALR(1) parsers it is not limited to “well-behaved” grammars. Unlike other general context-free parsing algorithms such as CYK, it does not devote time and space to operations which can be seen in advance to have no possible use in a full parse. Earley’s procedural description involves successive changes to a small set of data structures representing sets of Earley items; these procedural changes cannot be translated directly into a functional language lacking assignment. But Earley’s data-structure updates can be understood as defining relations among Earley items, and the algorithm as a whole can be interpreted as calculating the smallest set of Earley items which contains a given starter item and is closed over a small number of relations on items. Re-thinking the Earley algorithm in this way not only makes it easier to implement it in XSLT and XQuery, but helps make it clear why the parser is both complete (it will always find a parse if there is one) and correct (any parse it finds will be a real parse).

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

scholarly journals Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Witold Dyrka ◽  
Marlena Gąsior-Głogowska ◽  
Monika Szefczyk ◽  
Natalia Szulc
Keyword(s):  
Functional Relationship ◽  
High Sensitivity ◽  
Alternative Methods ◽  
Discriminative Power ◽  
Context Free Grammar ◽  
Protein Motifs ◽  
Functional Features ◽  
Universal Grammars ◽  
Context Free ◽  
Probabilistic Context

Abstract Background Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs. Results First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship. Conclusions While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

A generalization of the concept of a context-free grammar

Cybernetics ◽  
1974 ◽  
Vol 8 (3) ◽  
pp. 349-351
Author(s):  
A. A. Letichevskii
Keyword(s):  
Context Free Grammar ◽  
Context Free

scholarly journals Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language

2013 ◽  
Vol 39 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Alexander Fraser ◽  
Helmut Schmid ◽  
Richárd Farkas ◽  
Renjing Wang ◽  
Hinrich Schütze
Keyword(s):  
State Of The Art ◽  
Lessons Learned ◽  
Knowledge Sources ◽  
Lexical Knowledge ◽  
Context Free Grammar ◽  
The Impact ◽  
Context Free ◽  
Probabilistic Context

We study constituent parsing of German, a morphologically rich and less-configurational language. We use a probabilistic context-free grammar treebank grammar that has been adapted to the morphologically rich properties of German by markovization and special features added to its productions. We evaluate the impact of adding lexical knowledge. Then we examine both monolingual and bilingual approaches to parse reranking. Our reranking parser is the new state of the art in constituency parsing of the TIGER Treebank. We perform an analysis, concluding with lessons learned, which apply to parsing other morphologically rich and less-configurational languages.

scholarly journals Context-free grammar forms with strict interpretations

1980 ◽  
Vol 21 (1) ◽  
pp. 110-135 ◽  
Author(s):  
H.A. Maurer ◽  
A. Salomaa ◽  
D. Wood
Keyword(s):  
Context Free Grammar ◽  
Context Free
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