An Evaluation of Constituency-Based Hyponymy Extraction from Privacy Policies

Evans, Morgan C.; Bhatia, Jaspreet; Wadkar, Sudarshan; Breaux, Travis D.

doi:10.1109/re.2017.87

Cited by 24 publications

(9 citation statements)

References 19 publications

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“…In relation to legal requirements, other strands of work (Bhatia et al, 2016a,b;Evans et al, 2017) apply constituency and dependency parsing for analyzing privacy policies. Even though these approaches provide useful inspiration, our objective is different.…”

Section: Constituency and Dependency Parsing In Rementioning

confidence: 99%

An automated framework for the extraction of semantic legal metadata from legal texts

et al. 2021

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Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4%, and recall scores of 94.9% and 92.4%.

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Section: Constituency and Dependency Parsing In Rementioning

confidence: 99%

An automated framework for the extraction of semantic legal metadata from legal texts

et al. 2021

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“…Prior work on text parsing uses Tregex, which is a utility developed by Levy and Andrew for matching patterns in constituent trees [37]. For example, Evans et al evaluated the performance of Tregex on privacy policies [38]. However, Deep Learning (DL) API compilation error messages are domain specific.…”

Section: Error Message Understandingmentioning

confidence: 99%

SOAR: A Synthesis Approach for Data Science API Refactoring

Ramos

Yang

et al. 2021

Preprint

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With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source organizations often have to exert manual effort to refactor the APIs used in the code base. Moreover, due to the abundance of similar open-source libraries, data scientists working on a certain application may have an abundance of libraries to choose, maintain and migrate between. The manual refactoring between APIs is a tedious and error-prone task. Although recent research efforts were made on performing automatic API refactoring between different languages, previous work relies on statistical learning with collected pairwise training data for the API matching and migration. Using large statistical data for refactoring is not ideal because such training data will not be available for a new library or a new version of the same library. We introduce Synthesis for Open-Source API Refactoring (SOAR), a novel technique that requires no training data to achieve API migration and refactoring. SOAR relies only on the documentation that is readily available at the release of the library to learn API representations and mapping between libraries. Using program synthesis, SOAR automatically computes the correct configuration of arguments to the APIs and any glue code required to invoke those APIs. SOAR also uses the interpreter's error messages when running refactored code to generate logical constraints that can be used to prune the search space. Our empirical evaluation shows that SOAR can successfully refactor 80% of our benchmarks corresponding to deep learning models with up to 44 layers with an average run time of 97.23 seconds, and 90% of the data wrangling benchmarks with an average run time of 17.31 seconds.

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“…In relation to legal requirements specifically, Bhatia et al [9], [39] and Evans et al [40] apply constituency and dependency parsing for analyzing privacy policies. These threads of work have provided us with useful inspiration.…”

Section: Constituency and Dependency Parsing In Rementioning

confidence: 99%

Automated Extraction of Semantic Legal Metadata using Natural Language Processing

Sleimi

Sannier

Sabetzadeh

et al. 2018

2018 IEEE 26th International Requirements Engineering Conference (RE)

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Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal requirements.[Objectives] Our work is motivated by two observations: (1) The existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis.(2) Automated support for the extraction of semantic legal metadata is scarce, and further does not exploit the full potential of natural language processing (NLP). Our objective is to take steps toward addressing these limitations.[Methods] We review and reconcile the semantic legal metadata types proposed in RE. Subsequently, we conduct a qualitative study aimed at investigating how the identified metadata types can be extracted automatically.[Results and Conclusions] We propose (1) a harmonized conceptual model for the semantic metadata types pertinent to legal requirements analysis, and (2) automated extraction rules for these metadata types based on NLP. We evaluate the extraction rules through a case study. Our results indicate that the rules generate metadata annotations with high accuracy.

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An Evaluation of Constituency-Based Hyponymy Extraction from Privacy Policies

Cited by 24 publications

References 19 publications

An automated framework for the extraction of semantic legal metadata from legal texts

An automated framework for the extraction of semantic legal metadata from legal texts

SOAR: A Synthesis Approach for Data Science API Refactoring

Automated Extraction of Semantic Legal Metadata using Natural Language Processing

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