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Found 21 papers, 0 papers with code
IRNLP_DAIICT@LT-EDI-EACL2021: Hope Speech detection in Code Mixed text using TF-IDF Char N-grams and MuRIL
The aim of this shared task is to identify hope speech from a code-mixed data-set of YouTube comments.
IRLAB-DAIICT@DravidianLangTech-EACL2021: Neural Machine Translation
This paper describes our team’s submission of the EACL DravidianLangTech-2021’s shared task on Machine Translation of Dravidian languages. We submitted our translations for English to Malayalam , Tamil , Telugu and also Tamil-Telugu language pairs.
IRNLP_DAIICT@DravidianLangTech-EACL2021:Offensive Language identification in Dravidian Languages using TF-IDF Char N-grams and MuRIL
This paper presents the participation of the IRNLPDAIICT team from Information Retrieval and Natural Language Processing lab at DA-IICT, India in DravidianLangTech-EACL2021 Offensive Language identification in Dravidian Languages.
Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023
The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels.
Named Entity Recognition Based Automatic Generation of Research Highlights
We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights.
Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages
This paper presents the HASOC subtrack for English, Hindi, and Marathi.
LawSum: A weakly supervised approach for Indian Legal Document Summarization
Some possible applications of this dataset besides legal document summarization can be in retrieval, citation analysis and prediction of decisions by a particular judge.
Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages
HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY.
IRLab\_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets
The paper describes systems that our team IRLab{\_}DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020.
IRLab\_DAIICT at SemEval-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification
The paper describes systems that our team IRLab{\_}DAIICT employed for shared task OffensEval2020: Multilingual Offensive Language Identification in Social Media shared task.
DA-LD-Hildesheim at SemEval-2019 Task 6: Tracking Offensive Content with Deep Learning using Shallow Representation
Since limited labeled data was available for the training, pre-trained word vectors are used and fine-tuned on this classification task.
An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression
Results show that text representation using BoW performs better than word embedding on machine learning classifiers.
Exploiting local and global performance of candidate systems for aggregation of summarization techniques
With an ever growing number of extractive summarization techniques being proposed, there is less clarity then ever about how good each system is compared to the rest.
Filtering Aggression from the Multilingual Social Media Feed
Using the validation data, we found that validation accuracy of our deep learning models outperform all standard machine learning classifiers and voting based ensemble techniques and results on test data support these findings.
Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data
We propose a new attention based deep learning architecture that jointly learns to identify important content, as well as the cue phrases that are indicative of summary worthy sentences.
Content based Weighted Consensus Summarization
In this paper, we describe a category of ensemble systems which use consensus between the candidate systems to build a better meta-summary.
Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems
In order to prove the conjecture, the performance of one of the best dependence models is compared to several well established algorithms in text classification.
Formal Ontology Learning on Factual IS-A Corpus in English using Description Logics
Hence, we also claim that such sentences requires special studies in the context of OL before any truly formal OL can be proposed.
DLOLIS-A: Description Logic based Text Ontology Learning
Researchers in this field have been motivated by the possibility of automatically building a knowledge base on top of text documents so as to support reasoning based knowledge extraction.
