Incremental parsing with parallel multiple context-free grammars

Angelov, Krasimir

doi:10.3115/1609067.1609074

Cited by 19 publications

(17 citation statements)

References 10 publications

(6 reference statements)

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“…The algorithm is an extension of the algorithm by Angelov (2009; which adds statistical ranking. This is a top-down algorithm, shown by Ljunglöf (2012) to be similar to other top-down algorithms (Burden and Ljunglöf, 2005;Kanazawa, 2008;Kallmeyer and Maier, 2009).…”

Section: Introductionmentioning

confidence: 99%

Fast Statistical Parsing with Parallel Multiple Context-Free Grammars

Angelov¹,

Ljunglöf²

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by Angelov (2009) to which we added statistical ranking. We show that the new algorithm is several times faster than other statistical PMCFG parsing algorithms on real-sized grammars. At the same time the algorithm is more general since it supports non-binarized and non-linear grammars.We also show that if we make the search heuristics non-admissible, the parsing speed improves even further, at the risk of returning sub-optimal solutions.

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Section: Introductionmentioning

confidence: 99%

Fast Statistical Parsing with Parallel Multiple Context-Free Grammars

Angelov¹,

Ljunglöf²

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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“…If our grammar does manage to parse a sentence from a real text, it happens more as a lucky coincidence than by design. (This is not so bad as it might sound, since parsing in GF is predictive, which means that the user input is guided by word suggestions and completions; see [31]. )…”

Section: The Dynamicity Of Languagementioning

confidence: 99%

Translating between Language and Logic: What Is Easy and What Is Difficult

Ranta

2011

Lecture Notes in Computer Science

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Abstract. Natural language interfaces make formal systems accessible in informal language. They have a potential to make systems like theorem provers more widely used by students, mathematicians, and engineers who are not experts in logic. This paper shows that simple but still useful interfaces are easy to build with available technology. They are moreover easy to adapt to different formalisms and natural languages. The language can be made reasonably nice and stylistically varied. However, a fully general translation between logic and natural language also poses difficult, even unsolvable problems. This paper investigates what can be realistically expected and what problems are hard.

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“…Overgeneralization occurs when the inference process produces a grammar whose language is larger than the unknown language. The use of negligible items results in an unnecessarily evolutionary GA-based [42] L A g t s [ 8] heuristic ALLiS [13] Inductive CYK [36] ABL [54] MDL e-GRIDS [38] CDC [10] VEGGIE [4,5] Eiland et al [17] greedy search ADIOS CDC Incremental parsing [3,44] Sequitur [37] GraphViz [45,46] clustering EMILE [1] C D C large grammar. To limit the impact of over-generalization, it is recommended to also use a set of negative examples.…”

Section: Grammar Inferencementioning

confidence: 99%

SEQUIN: a grammar inference framework for analyzing malicious system behavior

Luh

Schramm

Wagner

et al. 2018

J Comput Virol Hack Tech

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Targeted attacks on IT systems are a rising threat to the confidentiality of sensitive data and the availability of critical systems. The emergence of Advanced Persistent Threats (APTs) made it paramount to fully understand the particulars of such attacks in order to improve or devise effective defense mechanisms. Grammar inference paired with visual analytics (VA) techniques offers a powerful foundation for the automated extraction of behavioral patterns from sequential event traces. To facilitate the interpretation and analysis of APTs, we present SEQUIN, a grammar inference system based on the Sequitur compression algorithm that constructs a context-free grammar (CFG) from string-based input data. In addition to recursive rule extraction, we expanded the procedure through automated assessment routines capable of dealing with multiple input sources and types. This automated assessment enables the accurate identification of interesting frequent or anomalous patterns in sequential corpora of arbitrary quantity and origin. On the formal side, we extended the CFG with attributes that help describe the extracted (malicious) actions. Discovery-focused pattern visualization of the output is provided by our dedicated KAMAS VA prototype.

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Incremental parsing with parallel multiple context-free grammars

Cited by 19 publications

References 10 publications

Fast Statistical Parsing with Parallel Multiple Context-Free Grammars

Fast Statistical Parsing with Parallel Multiple Context-Free Grammars

Translating between Language and Logic: What Is Easy and What Is Difficult

SEQUIN: a grammar inference framework for analyzing malicious system behavior

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