scholarly journals Spurious Ambiguity and Focalization

2018 ◽  
Vol 44 (2) ◽  
pp. 285-327 ◽  
Author(s):  
Glyn Morrill ◽  
Oriol Valentín
Keyword(s):  
Mathematical Structure ◽  
Search Space ◽  
Categorial Grammar ◽  
Context Free Grammar ◽  
Ordered Trees ◽  
Proof Nets ◽  
Theoretic Technique ◽  
Context Free

Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize. In this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.

Robust garden path parsing

2004 ◽  
Vol 10 (1) ◽  
pp. 1-24 ◽  
Author(s):  
BRIAN ROARK
Keyword(s):  
Ad Hoc ◽  
Search Space ◽  
Complete Coverage ◽  
Context Free Grammar ◽  
Garden Path ◽  
Empirical Results ◽  
Newspaper Text ◽  
Context Free ◽  
Probabilistic Context ◽  
Excellent Accuracy

This paper presents modifications to a standard probabilistic context-free grammar that enable a predictive parser to avoid garden pathing without resorting to any ad-hoc heuristic repair. The resulting parser is shown to apply efficiently to both newspaper text and telephone conversations with complete coverage and excellent accuracy. The distribution over trees is peaked enough to allow the parser to find parses efficiently, even with the much larger search space resulting from overgeneration. Empirical results are provided for both Wall St. Journal and Switchboard test corpora.

scholarly journals Unsupervised Grammar Induction with Depth-bounded PCFG

2018 ◽  
Vol 6 ◽  
pp. 211-224 ◽  
Author(s):  
Lifeng Jin ◽  
Finale Doshi-Velez ◽  
Timothy Miller ◽  
William Schuler ◽  
Lane Schwartz
Keyword(s):  
Parameter Space ◽  
Search Space ◽  
Grammar Induction ◽  
Context Free Grammar ◽  
Grammar Acquisition ◽  
Recent Interest ◽  
Category Labels ◽  
Context Free ◽  
Probabilistic Context

There has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.

scholarly journals QoS-aware service composition based on context-free grammar and skyline in service function chaining using genetic algorithm

2021 ◽  
Vol 7 ◽  
pp. e603
Author(s):  
Pouya Khosravian ◽  
Sima Emadi ◽  
Ghasem Mirjalily ◽  
Behzad Zamani
Keyword(s):  
Genetic Algorithm ◽  
Service Composition ◽  
Service Providers ◽  
Task Force ◽  
Mobile Network ◽  
Search Space ◽  
Context Free Grammar ◽  
Service Function ◽  
Novel Approach ◽  
Context Free

Service function chaining (SFC) is a mechanism that allows service providers to combine various service functions and exploit the available virtual infrastructure. The best selection of virtual services in the network is essential for meeting user requirements and constraints. This paper proposes a novel approach to generate the optimal composition of the service functions. To this end, a genetic algorithm based on context-free grammar (CFG) that adheres to the Internet Engineering Task Force (IETF) standard and Skyline was developed to use in SFC. The IETF uses cases of the data center, security, and mobile network filtered out the invalid service chains, which resulted in reduced search space. The proposed genetic algorithm found the Skyline service chain instance with the highest quality. The genetic operations were defined to ensure that the service function chains generated in the algorithm process were standard. The experimental results showed that the proposed service composition method outperformed the other methods regarding the quality of service (QoS), running time, and time complexity metrics. Ultimately, the proposed CFG could be generalized to other SFC use cases.

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.

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