A Machine Learning Filter for the Slot Filling Task

Cesare, Kevin Lange Di; Zouaq, Amal; Gagnon, Michel; Jean-Louis, Ludovic

doi:10.3390/info9060133

Cited by 2 publications

(1 citation statement)

References 24 publications

(30 reference statements)

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“…In general, facts (also called relational facts) extracted from unstructured text data appear in the form of a subject‐predicate‐object triple. These triplets form the main elements in the construction and enrichment of knowledge‐bases, which in turn facilitate semantic‐based information retrieval (Lange Di Cesare, Zouaq, Gagnon, & Jean‐Louis, 2018; Nakashole, Theobald, & Weikum, 2011; Surdeanu, 2013). Extracting novel information about entities in text boosts the performance of open‐domain factoid based question answering systems (Ranjan & Balabantaray, 2016).…”

Section: Introductionmentioning

confidence: 99%

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Thomas

Sangeetha

2021

Expert Systems

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Tremendous growth in the availability of judicial documents has demanded the rise of information extraction (IE) techniques that support the automatic extraction of relevant concepts or data from judicial texts. Among various approaches available for IE, ontology‐based IE has proven to be the most appropriate for extracting domain‐specific information from natural language text. Through this article, we propose a knowledge‐driven, semi‐supervised pattern‐based learning (bootstrapping) approach to extract domain‐specific facts from judicial text, starting with a small set of seed facts. In the semantic analysis of legal text, fact extraction is the next step to entity identification, which involves the identification of roles played by each entity in the judicial text. The proposed methodology learns extraction patterns for 12 classes of facts from the judicial text through the integration of the domain ontology called judicial case ontology (JCO). The experimental results were evaluated by human judges and found to be quite promising. One main feature of the proposed methodology is its portability across various domains (such as medical, banking, insurance, etc.) which in turn helps build expert systems in various sectors.

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Section: Introductionmentioning

confidence: 99%

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Thomas

Sangeetha

2021

Expert Systems

View full text Add to dashboard Cite

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Analysis of Digital Information in Storage Devices Using Supervised and Unsupervised Natural Language Processing Techniques

2023

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Due to the advancement of technology, cybercrime has increased considerably, making digital forensics essential for any organisation. One of the most critical challenges is to analyse and classify the information on devices, identifying the relevant and valuable data for a specific purpose. This phase of the forensic process is one of the most complex and time-consuming, and requires expert analysts to avoid overlooking data relevant to the investigation. Although tools exist today that can automate this process, they will depend on how tightly their parameters are tuned to the case study, and many lack support for complex scenarios where language barriers play an important role. Recent advances in machine learning allow the creation of new architectures to significantly increase the performance of information analysis and perform the intelligent search process automatically, reducing analysis time and identifying relationships between files based on initial parameters. In this paper, we present a bibliographic review of artificial intelligence algorithms that allow an exhaustive analysis of multimedia information contained in removable devices in a forensic process, using natural language processing and natural language understanding techniques for the automatic classification of documents in seized devices. Finally, some of the open challenges technology developers face when generating tools that use artificial intelligence techniques to analyse the information contained in documents on seized devices are reviewed.

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A Machine Learning Filter for the Slot Filling Task

Cited by 2 publications

References 24 publications

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Analysis of Digital Information in Storage Devices Using Supervised and Unsupervised Natural Language Processing Techniques

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