scholarly journals Overview of COLIEE 2017

10.29007/fm8f ◽  
2018 ◽  
Author(s):  
Yoshinobu Kano ◽  
Mi-Young Kim ◽  
Randy Goebel ◽  
Ken Satoh
Keyword(s):  
Information Retrieval ◽  
Information Extraction ◽  
Question Answering ◽  
Retrieval Task ◽  
Legal Information ◽  
Legal Question

We present the evaluation of the legal question answering Competition on Legal Information Extraction/Entailment (COLIEE) 2017. The COLIEE 2017 Task consists of two sub-Tasks: legal information retrieval (Task 1), and recognizing entailment between articles and queries (Task 2). Participation was open to any group based on any approach, and the tasks attracted 10 teams. We received 9 submissions to Task 1 (for a total of 17 runs), and 8 submissions to Task 2 (for a total of 20 runs).

scholarly journals Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities

10.29007/2xzw ◽  
2018 ◽  
Author(s):  
Danilo S. Carvalho ◽  
Vu Tran ◽  
Khanh Van Tran ◽  
Nguyen Le Minh
Keyword(s):  
Information Retrieval ◽  
Question Answering ◽  
High Efficiency ◽  
Legal Information ◽  
Legal Professionals ◽  
Lexical Statistics ◽  
Term Order ◽  
Legal Document ◽  
N Gram ◽  
Analysis Of Error

Legal professionals worldwide are currently trying to get up-to-pace with the explosive growth in legal document availability through digital means. This drives a need for high efficiency Legal Information Retrieval (IR) and Question Answering (QA) methods. The IR task in particular has a set of unique challenges that invite the use of semantic motivated NLP techniques. In this work, a two-stage method for Legal Information Retrieval is proposed, combining lexical statistics and distributional sentence representations in the context of Competition on Legal Information Extraction/Entailment (COLIEE). The combination is done with the use of disambiguation rules, applied over the rankings obtained through n-gram statistics. After the ranking is done, its results are evaluated for ambiguity, and disambiguation is done if a result is decided to be unreliable for a given query. Competition and experimental results indicate small gains in overall retrieval performance using the proposed approach. Additionally, an analysis of error and improvement cases is presented for a better understanding of the contributions.

scholarly journals Legal Information Retrieval Using Topic Clustering and Neural Networks

10.29007/psgx ◽  
2018 ◽  
Author(s):  
Rohan Nanda ◽  
Adebayo Kolawole John ◽  
Luigi Di Caro ◽  
Guido Boella ◽  
Livio Robaldo
Keyword(s):  
Information Retrieval ◽  
Short Term Memory ◽  
String Matching ◽  
Clustering Method ◽  
Short Term ◽  
Retrieval Task ◽  
Legal Information ◽  
Textual Entailment ◽  
Topic Clustering ◽  
Long Short Term Memory

This paper presents a description about our adopted approach for the information retrieval and textual entailment tasks of the COLIEE 2017 competition. We address the information retrieval task by implementing a partial string matching and a topic clustering method. For the textual entailment task, we propose a Long Short-Term Memory (LSTM) - Convolutional Neural Network (CNN) model which utilizes word embeddings trained on the Google News vectors. We evaluated our approach for both tasks on the COLIEE 2017 dataset. The results demonstrate that the topic clustering method outperformed the partial string matching method in the information retrieval task. The performance of LSTM-CNN model was competitive with other textual entailment systems.

scholarly journals Evaluation of Diversification Techniques for Legal Information Retrieval

Algorithms ◽  
2017 ◽  
Vol 10 (1) ◽  
pp. 22 ◽  
Author(s):  
Marios Koniaris ◽  
Ioannis Anagnostopoulos ◽  
Yannis Vassiliou
Keyword(s):  
Information Retrieval ◽  
Legal Information ◽  
Diversification Techniques

Apply Bert-based models and Domain knowledge for Automated Legal Question Answering tasks at ALQAC 2021

2021 ◽  
Author(s):  
Truong-Thinh Tieu ◽  
Chieu-Nguyen Chau ◽  
Nguyen-Minh-Hoang Bui ◽  
Truong-Son Nguyen ◽  
Le-Minh Nguyen
Keyword(s):  
Domain Knowledge ◽  
Question Answering ◽  
Legal Question

scholarly journals QA4IE: A Question Answering Based Framework for Information Extraction

2018 ◽  
pp. 198-216 ◽  
Author(s):  
Lin Qiu ◽  
Hao Zhou ◽  
Yanru Qu ◽  
Weinan Zhang ◽  
Suoheng Li ◽  
...  
Keyword(s):  
Information Extraction ◽  
Question Answering
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