Search Results for author: Nisansa de Silva

Found 19 papers, 2 papers with code

Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks

no code implementations EACL 2021 Nisansa de Silva, Dejing Dou

Social networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any individual the power to post anything.

Semantic Similarity Semantic Textual Similarity

SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts

no code implementations12 Nov 2020 Chanika Ruchini Mudalige, Dilini Karunarathna, Isanka Rajapaksha, Nisansa de Silva, Gathika Ratnayaka, Amal Shehan Perera, Ramesh Pathirana

A number of publicly available datasets for a wide range of domains usually fulfill the needs of researchers to perform their studies in the field of ABSA.

Aspect-Based Sentiment Analysis

Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting

no code implementations PACLIC 2020 Gathika Ratnayaka, Nisansa de Silva, Amal Shehan Perera, Ramesh Pathirana

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.

Sentiment Analysis

Sinhala Language Corpora and Stopwords from a Decade of Sri Lankan Facebook

1 code implementation15 Jul 2020 Yudhanjaya Wijeratne, Nisansa de Silva

This paper presents two colloquial Sinhala language corpora from the language efforts of the Data, Analysis and Policy team of LIRNEasia, as well as a list of algorithmically derived stopwords.

Shift-of-Perspective Identification Within Legal Cases

no code implementations6 Jun 2019 Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Viraj Salaka Gamage, Menuka Warushavithana, Amal Shehan Perera

Therefore, the process of automatic information extraction from documents containing legal opinions related to court cases can be considered to be of significant importance.

Open Information Extraction Sentiment Analysis

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

1 code implementation5 Jun 2019 Nisansa de Silva

Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka.

Logic Rules Powered Knowledge Graph Embedding

no code implementations9 Mar 2019 Pengwei Wang, Dejing Dou, Fangzhao Wu, Nisansa de Silva, Lianwen Jin

And then, to put both triples and mined logic rules within the same semantic space, all triples in the knowledge graph are represented as first-order logic.

Knowledge Graph Embedding Link Prediction +1

Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations

no code implementations10 Sep 2018 Gathika Ratnayaka, Thejan Rupasinghe, Nisansa de Silva, Menuka Warushavithana, Viraj Gamage, Amal Shehan Perera

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.

Legal Document Retrieval using Document Vector Embeddings and Deep Learning

no code implementations27 May 2018 Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal, Madhavi Perera

The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process.

Information Retrieval Semantic Similarity +2

Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings

no code implementations9 Sep 2017 Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha, Madhavi Perera

With the use of word embeddings in the field of natural language processing, it became a popular topic due to its ability to cope up with semantic sensitivity.

Word Embeddings

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