Answering questions about European legislation

Rodrigo, Álvaro; Pérez-Iglesias, Joaquín; Peñas, Anselmo; Garrido, Guillermo; Araujo, Lourdes

doi:10.1016/j.eswa.2013.05.008

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…In parallel, (Tran et al, 2013) proposed mining reference information. Last but not least, (Rodrigo et al, 2013) proposed a new IR-based system with the lessons learned from ResPubliQA.…”

Section: Classical Ir-based Solutionsmentioning

confidence: 99%

A Survey on Legal Question Answering Systems

Martinez‐Gil¹

2021

Preprint

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Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities.The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.

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“…In parallel, (Tran et al, 2013) proposed mining reference information. Last but not least, (Rodrigo et al, 2013) proposed a new IR-based system with the lessons learned from ResPubliQA.…”

Section: Classical Ir-based Solutionsmentioning

confidence: 99%

A Survey on Legal Question Answering Systems

Martinez‐Gil¹

2021

Preprint

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“…The answers to questions starting with the wh-words (What, When, Which, Where, Who) [7]) are generally NEs. So, incorporating NER in QA systems [8][9][10][11] makes the task of finding answers to questions considerably easier.…”

mentioning

confidence: 99%

Enhancing the Performance of Telugu Named Entity Recognition Using Gazetteer Features

2020

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Named entity recognition (NER) is a fundamental step for many natural language processing tasks and hence enhancing the performance of NER models is always appreciated. With limited resources being available, NER for South-East Asian languages like Telugu is quite a challenging problem. This paper attempts to improve the NER performance for Telugu using gazetteer-related features, which are automatically generated using Wikipedia pages. We make use of these gazetteer features along with other well-known features like contextual, word-level, and corpus features to build NER models. NER models are developed using three well-known classifiers—conditional random field (CRF), support vector machine (SVM), and margin infused relaxed algorithms (MIRA). The gazetteer features are shown to improve the performance, and theMIRA-based NER model fared better than its counterparts SVM and CRF.

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