Legal Document Retrieval Using Document Vector Embeddings and Deep Learning

Sugathadasa, Keet; Ayesha, Buddhi; Silva, Nisansa de; Perera, Amal Shehan; Jayawardana, Vindula; Perera, Madhavi

doi:10.1007/978-3-030-01177-2_12

Cited by 52 publications

(33 citation statements)

References 36 publications

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“…There are various applications of text similarity measures [2] which includes automatic text summarization, relevance feedback classification, automatic evaluation of machine translation and determining text coherence. There are various approaches which are used to calculate the similarity measure which are based on statistical methods, vector representation of words in the given document, string or corpus based approach and hybrid similarity measures.…”

Section: Semantic Similarity Measuresmentioning

confidence: 99%

Legal Document Summarization Using Nlp and Ml Techniques

Kore¹,

Ray

Lade

et al. 2020

int. jour. eng. com. sci

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Reading legal documents are tedious and sometimes it requires domain knowledge related to that document. It is hard to read the full legal document without missing the key important sentences. With increasing number of legal documents it would be convenient to get the essential information from the document without having to go through the whole document. The purpose of this study is to understand a large legal document within a short duration of time. Summarization gives flexibility and convenience to the reader. Using vector representation of words, text ranking algorithms, similarity techniques, this study gives a way to produce the highest ranked sentences. Summarization produces the result in such a way that it covers the most vital information of the document in a concise manner. The paper proposes how the different natural language processing concepts can be used to produce the desired result and give readers the relief from going through the whole complex document. This study definitively presents the steps that are required to achieve the aim and elaborates all the algorithms used at each and every step in the process.

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Section: Semantic Similarity Measuresmentioning

confidence: 99%

Legal Document Summarization Using Nlp and Ml Techniques

Kore¹,

Ray

Lade

et al. 2020

int. jour. eng. com. sci

View full text Add to dashboard Cite

show abstract

“…The processes related to Information Extraction creates new challenges each time they are being applied to a new domain, due to the domain-specific nature of the text and documents. The legal domain can be considered as such a challenging domain when it comes to Natural Language Processing, mainly due to the nature of legal documents, which employ a vocabulary of mixed origin ranging from Latin to English [5]. This challenging nature has stimulated the emergence of legal domain specific works related to different areas such as information extraction [3], information organization [2], [4] and sentiment analysis [13].…”

Section: Examplementioning

confidence: 99%

Classifying Sentences in Court Case Transcripts using Discourse and Argumentative Properties

Rathnayake

Rupasinghe

Silva

et al. 2019

Int J on Adv. in ICT for Emerging Countries

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Information that is available in court case transcripts which describes the proceedings of previous legal cases are of significant importance to legal officials. Therefore, automatic information extraction from court case transcripts can be considered as a task of huge importance when it comes to facilitating the processes related to the legal domain. A sentence can be considered as a fundamental textual unit of any document which is made up of text. Therefore, analyzing the properties of sentences can be of immense value when it comes to information extraction from machine-readable text. This paper demonstrates how the properties of sentences can be used to extract valuable information from court case transcripts. As the first task, the sentence pairs were classified based on the relationship type which can be observed between the two sentences. There, we defined relationship types that can be observed between sentences in court case transcripts. A system combining a machine learning model and a rule-based approach was used to classify pairs of sentences according to the relationship type. The next classification task was performed based on whether a given sentence provides a legal argument or not. The results obtained through the proposed methodologies were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts. Similarly, this study provides novel and effective approaches to identify argumentative sentences in court case transcripts.

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“…Sugathadesa. K. et al [1] described a legal document retrieval using document embeddings and deep learning. Here, the authors developed three major models, that is vector space representations of legal system by using Node2Vec algorithm for first model, sentence similarity for second model, and vector space for the third model.…”

Section: A Legal Search Datamentioning

confidence: 99%

An Artificial Intelligence based Analysis in Legal domain

2019

IJITEE

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Around the world, legitimate information and common laws are available in raw form, but hard to understand and not in organized form. All legitimate information is nowadays computerized since the legal information gets generated on a regular basis in a huge volume due to increase of maritime (law) courts. The automation tool to analyse this legal data can serve effectively for lawyers and law students, which can address a lawyer’s role and can even become powerful to release such a role in future. The machine learning and deep learning algorithmsbased analysis systems apply these methods mainly for document classification. Legal document translation, text classification, summarization, data forecasting and data obtainment are part of the goals got from research charity. In this study, we review about the different methods of deep learning used in legal tasks such as Legal data search, Legal document analytics, and Legal perspective interface. To solve aggregate tasks, one can use the deep learning methods like, Recurrent Network Networks (RNN), Gated Recurrent unit network (GRU), Long Short Term Memory networks (LSTM), Convolutional neural network (CNN). Through this review, we instituted that deep learning models are giving advanced performance

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Legal Document Retrieval Using Document Vector Embeddings and Deep Learning

Cited by 52 publications

References 36 publications

Legal Document Summarization Using Nlp and Ml Techniques

Legal Document Summarization Using Nlp and Ml Techniques

Classifying Sentences in Court Case Transcripts using Discourse and Argumentative Properties

An Artificial Intelligence based Analysis in Legal domain

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