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Found 38 papers, 7 papers with code
Legal Case Winning Party Prediction With Domain Specific Auxiliary Models
The model is built with and experimented using legal domain specific sub-models to provide more visibility to the final model, along with other variations.
Automatic Generation of Abstracts for Research Papers
Summarizing has always been an important utility for reading long documents.
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews
Our hybrid model Instruct - DeBERTa uses SOTA InstructABSA for aspect extraction and DeBERTa-V3-baseabsa-V1 for aspect sentiment classification.
M2DS: Multilingual Dataset for Multi-document Summarisation
This paper introduces M2DS, emphasising its unique multilingual aspect, and includes baseline scores from state-of-the-art MDS models evaluated on our dataset.
Aspect-Based Sentiment Analysis Techniques: A Comparative Study
Since the dawn of the digitalisation era, customer feedback and online reviews are unequivocally major sources of insights for businesses.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
SHADE: Semantic Hypernym Annotator for Domain-specific Entities -- DnD Domain Use Case
Such an app can ensure that the notations are consistent, and the labels can be pre-defined or restricted reducing the room for errors.
Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research
We analysed a sample of NLP research papers archived in ACL Anthology as an attempt to quantify the degree of openness and the benefit of such an open culture in the NLP community.
Fine Tuning Named Entity Extraction Models for the Fantasy Domain
This work uses available lore of monsters in the D&D domain to fine-tune Trankit, which is a prolific NER framework that uses a pre-trained model for NER.
Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora
We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil).
Sinhala-English Word Embedding Alignment: Introducing Datasets and Benchmark for a Low Resource Language
In this paper, we try to align Sinhala and English word embedding spaces based on available alignment techniques and introduce a benchmark for Sinhala language embedding alignment.
Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain
Many NLP tasks, although well-resolved for general English, face challenges in specific domains like fantasy literature.
Multi-document Summarization: A Comparative Evaluation
This work serves as a reference for future MDS research and contributes to the development of accurate and robust models which can be utilized on demanding datasets with academically and/or scientifically complex data as well as generalized, relatively simple datasets.
Sinhala-English Parallel Word Dictionary Dataset
However, in the cases where one of the considered language pairs is a low-resource language, the existing top-down parallel data such as corpora are lacking in both tally and quality due to the dearth of human annotation.
Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons
This paper introduces the Forgotten Realms Wiki (FRW) data set and domain specific natural language generation using FRW along with related analyses.
Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages
In the process of numerically modeling natural languages, developing language embeddings is a vital step.
Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World
Using an existing language categorisation based on speaker population and vitality, we analyse the distribution of language data resources, amount of NLP/CL research, inclusion in multilingual web-based platforms and the inclusion in pre-trained multilingual models.
Selecting Seed Words for Wordle using Character Statistics
Wordle, a word guessing game rose to global popularity in the January of 2022.
Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data
The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand.
Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts
This paper uses millions of such reactions, derived from a decade worth of Facebook post data centred around a Sri Lankan context, to model an eye of the beholder approach to sentiment detection for online Sinhala textual content.
Critical Sentence Identification in Legal Cases Using Multi-Class Classification
Inherently, the legal domain contains a vast amount of data in text format.
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks
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.
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages.
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization
Network representation learning (NRL) is crucial in the area of graph learning.
SigmaLaw-ABSA: Dataset for Aspect-Based Sentiment Analysis in Legal Opinion Texts
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
Aspect-Based Sentiment Analysis (ABSA)
Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts
A document which elaborates opinions and arguments related to the previous court cases is known as a legal opinion text.
Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting
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.
Sinhala Language Corpora and Stopwords from a Decade of Sri Lankan Facebook
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
Therefore, the process of automatic information extraction from documents containing legal opinions related to court cases can be considered to be of significant importance.
Survey on Publicly Available Sinhala Natural Language Processing Tools and Research
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
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.
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning
This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain.
Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
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
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.
Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings
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.
Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings
Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks.
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity
Semantic similarity measures are an important part in Natural Language Processing tasks.
Subject Specific Stream Classification Preprocessing Algorithm for Twitter Data Stream
Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment.
