Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting
18872Authors
nan
Abstract
Analyzing the sentiments of legal opinions available in Legal Opinion Texts can facili- tate several use cases such as legal judgement prediction, contradictory statements identi- fication and party-based sentiment analysis. However, the task of developing a legal do- main specific sentiment annotator is challeng- ing due to resource constraints such as lack of domain specific labelled data and domain ex- pertise. In this study, we propose novel tech- niques that can be used to develop a sentiment annotator for the legal domain while minimiz- ing the need for manual annotations of data. 1
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Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting Gathika Ratnayaka*,1, Nisansa de Silva*, Amal Shehan Perera*, and Ramesh Pathirana+ *Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka +Faculty of Law, University of Colombo, Sri Lanka 1gathika.14@cse.mrt.ac.lk Abstract Analyzing the sentiments of legal opinions available in Legal Opinion Texts can facili- tate several use cases such as legal judgement prediction, contradictory statements identi- fication and party-based sentiment analysis. However, the task of developing a legal do- main specific sentiment annotator is challeng- ing due to resource constraints such as lack of domain specific labelled data and domain ex- pertise. In this study, we propose novel tech- niques that can be used to develop a sentiment annotator for the legal domain while minimiz- ing the need for manual annotations of data. 1 Introduction Legal Opinion Texts that elaborate on the incidents, arguments, legal opinions, and judgements associ- ated with previous court cases are an integral part of case law. As the information that can be acquired from these documents has the potential to be di- rectly applied in similar legal cases, legal officials use of them as information sources to support their arguments and opinions when handling a new legal scenario. Therefore, developing methodologies and tools that can be used to automatically extract valu- able information from legal opinion texts while de- riving useful insights from the extracted data are of significant importance when it comes to assisting le- gal officials via automated systems. Sentiment analysis can be considered as one such information extraction technique that has a signif- icant potential to facilitate various information ex- traction tasks. When a legal case is considered, it is built around two major parties that are opposing to each other. The party that brings forward the lawsuit is usually called the plaintiff and the other party is known as the defendant. At the beginning of a le- gal opinion text, a summary of the case is given, de- scribing the incidents associated with the case and also explaining how each party is related with those incidents. Legal opinions or the opinions of judges about the associated events and laws related to the court case can be considered as the most impor- tant type of information available in a legal opinion text. Such opinions may have a positive, neutral, or negative impact on a particular party. In addi- tion to the opinions that are directly related to the conduct of the parties, legal opinion texts also pro- vide interpretations related to previous judgements and also on statutes that are relevant to the legal case. Such opinions may elaborate on the justifi- cations, purposes, drawbacks and loopholes that are associated with a particular statute or a precedent. Moreover, the descriptions also contain information related to the proceeding of court cases such as ad- journment of the case and lack of evidence which can be considered as factors that can directly have an impact on the outcomes. When …
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Labeled Text
Analyzing the sentiments of legal opinions available in Legal Opinion Texts can facilitate several use cases such as legal judgement prediction, contradictory statements identification and party-based sentiment analysis. However, the task of developing a legal domain specific sentiment annotator is challenging due to resource constraints such as lack of domain specific labelled data and domain expertise. In this study, we propose novel techniques that can be used to develop a sentiment annotator for the legal domain while minimizing the need for manual annotations of data. 1
Publication Details
- Paper ID:
- 18872
- Year:
- 2020
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan