Autobank: a semi-automatic annotation tool for developing deep
            Minimalist Grammar treebanks

Torr, John

doi:10.18653/v1/e17-3021

Cited by 5 publications

(5 citation statements)

References 11 publications

(10 reference statements)

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“…Fig. 3(b) plots the percentage of exploits published earlier (or later) than the corresponding CVEs by six-day ranges [0, 1], [2,7], [8,30], [31,180], [180,365], and >365. We use negative numbers to represent earlier-than.…”

Section: B the Delay In Creating Cve Entriesmentioning

confidence: 99%

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Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization

Sun,

Xing,

Guo

et al. 2021

Preprint

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ExploitDB is one of the important public websites, which contributes a large number of vulnerabilities to official CVE database. Over 60% of these vulnerabilities have high-or critical-security risks. Unfortunately, over 73% of exploits appear publicly earlier than the corresponding CVEs, and about 40% of exploits do not even have CVEs. To assist in documenting CVEs for the ExploitDB posts, we propose an open information method to extract 9 key vulnerability aspects (vulnerable product/version/component, vulnerability type, vendor, attacker type, root cause, attack vector and impact) from the verbose and noisy ExploitDB posts. The extracted aspects from an ExploitDB post are then composed into a CVE description according to the suggested CVE description templates, which is must-provided information for requesting new CVEs. Through the evaluation on 13,017 manually labeled sentences and the statistically sampling of 3,456 extracted aspects, we confirm the high accuracy of our extraction method. Compared with 27,230 reference CVE descriptions. Our composed CVE descriptions achieve high ROUGH-L (0.38), a longest common subsequence based metric for evaluating text summarization methods.

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Section: B the Delay In Creating Cve Entriesmentioning

confidence: 99%

“…The validation confirms the high accuracy (>97%) of the identified entities. This type of semi-automatic labelling method is often used in the NLP community to create large-scale (but maybe noisy) corpora [31]- [33].…”

Section: Accuracy Of Aspect Extraction From Exploitsmentioning

confidence: 99%

Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization

Sun,

Xing,

Guo

et al. 2021

Preprint

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“…MGbank (Torr, 2017(Torr, , 2018) is a treebank of MG derivation trees constructed in part manually by hand-annotating a subset of PTB sentences and in part automatically using a parser equipped with the manually constructed grammar and guided by the corresponding PTB and CCGbank (Hockenmaier and Steedman, 2007) structures. The corpus was continuously machine tested for over-and undergeneration throughout its development.…”

Section: Mgbankmentioning

confidence: 99%

Wide-Coverage Neural A* Parsing for Minimalist Grammars

Torr¹,

Stanojević²,

Steedman³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky's (1995) Minimalist Program. This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing. The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014; Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees. We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies. Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is O(n 3). The parser is publicly available. 1 LAB

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“…Instead, it seems more promising to run sim-ulations where realistic MGs are automatically converted to SMNF. While such realistic MGs are currently being worked on (Torr, 2017), they are still in a highly preliminary stage. Hopefully some simulations will be feasible in the near future.…”

Section: Potential Cases Of Blow-upmentioning

confidence: 99%

Grammar Size and Quantitative Restrictions on Movement

Graf¹

2018

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Autobank: a semi-automatic annotation tool for developing deep Minimalist Grammar treebanks

Cited by 5 publications

References 11 publications

Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization

Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization

Wide-Coverage Neural A* Parsing for Minimalist Grammars

Grammar Size and Quantitative Restrictions on Movement

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