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See detailMULTIMODAL LEGAL INFORMATION RETRIEVAL
Adebayo, Kolawole John UL

Doctoral thesis (2018)

The goal of this thesis is to present a multifaceted way of inducing semantic representation from legal documents as well as accessing information in a precise and timely manner. The thesis explored ... [more ▼]

The goal of this thesis is to present a multifaceted way of inducing semantic representation from legal documents as well as accessing information in a precise and timely manner. The thesis explored approaches for semantic information retrieval (IR) in the legal context with a technique that maps specific parts of a text to the relevant concept. This technique relies on text segments, using the Latent Dirichlet Allocation (LDA), a topic modeling algorithm for performing text segmentation, expanding the concept using some Natural Language Processing techniques, and then associating the text segments to the concepts using a semi-supervised Text Similarity technique. This solves two problems, i.e., that of user specificity in formulating query, and information overload, for querying a large document collection with a set of concepts is more fine-grained since specific information, rather than full documents is retrieved. The second part of the thesis describes our Neural Network Relevance Model for E-Discovery Information Retrieval. Our algorithm is essentially a feature-rich Ensemble system with different component Neural Networks extracting different relevance signal. This model has been trained and evaluated on the TREC Legal track 2010 data. The performance of our models across board proves that it capture the semantics and relatedness between query and document which is important to the Legal Information Retrieval domain [less ▲]

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See detailAn approach to information retrieval and question answering in the legal domain
Adebayo, Kolawole John UL; Di Caro, Luigi; Boella, Guido et al

Scientific Conference (2016, November 15)

We describe in this paper, a report of our participation at COLIEE 2016 Information Retrieval (IR) and Legal Question Answering (LQA) tasks. Our solution for the IR part employs the use of a simple but ... [more ▼]

We describe in this paper, a report of our participation at COLIEE 2016 Information Retrieval (IR) and Legal Question Answering (LQA) tasks. Our solution for the IR part employs the use of a simple but effective Machine Learning (ML) procedure. Our Question Answering solution answers "YES or 'NO' to a question, i.e., 'YES' if the question is entailed by a text and 'NO' otherwise. With recent exploit of Multi-layered Neural Network systems at language modeling tasks, we presented a Deep Learning approach which uses an adaptive variant of the Long-Short Term Memory (LSTM), i.e. the Child Sum Tree LSTM (CST-LSTM) algorithm that we modified to suit our purpose. Additionally, we benchmarked this approach by handcrafting features for two popular ML algorithms, i.e., the Support Vector Machine (SVM) and the Random Forest (RF) algorithms. Even though we used some features that have performed well from similar works, we also introduced some semantic features for performance improvement. We used the results from these two algorithms as the baseline for our CST-LSTM algorithm. All evaluation was done on the COLIEE 2015 training and test sets. The overall result conforms the competitiveness of our approach. [less ▲]

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