Search Results for author:
Found 23 papers, 4 papers with code
SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts
In this paper, we propose an annotated sentiment analysis dataset made of informally written Bangla texts.
SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)
Divided into 13 tracks, the task focused on methods to identify complex named entities (like names of movies, products and groups) in 11 languages in both monolingual and multi-lingual scenarios.
Syntax and Themes: How Context Free Grammar Rules and Semantic Word Association Influence Book Success
By analyzing the model parameters, we extracted the successful semantic relationships from books of 12 different genres.
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks.
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs.
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings.
SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)
The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.
Multilingual Named Entity Recognition
named-entity-recognition
+3
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks
In this paper, we approach the problem of incrementally expanding MTL models' capability to solve new tasks over time by distilling the knowledge of an already trained model on n tasks into a new one for solving n+1 tasks.
Learning to Retrieve Engaging Follow-Up Queries
The retrieval system is trained on a dataset which contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates.
MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition
We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020).
LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation
To facilitate research in this direction, we propose a centralized benchmark for Linguistic Code-switching Evaluation (LinCE) that combines ten corpora covering four different code-switched language pairs (i. e., Spanish-English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian Arabic) and four tasks (i. e., language identification, named entity recognition, part-of-speech tagging, and sentiment analysis).
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues
Movies help us learn and inspire societal change.
BanFakeNews: A Dataset for Detecting Fake News in Bangla
In this work, we propose an annotated dataset of ~50K news that can be used for building automated fake news detection systems for a low resource language like Bangla.
Attending the Emotions to Detect Online Abusive Language
We experiment with this model on our dataset and later present the analysis.
Multi-view Story Characterization from Movie Plot Synopses and Reviews
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies.
Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies
In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts.
Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network
Folksonomy of movies covers a wide range of heterogeneous information about movies, like the genre, plot structure, visual experiences, soundtracks, metadata, and emotional experiences from watching a movie.
RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification
This paper presents our system for "TRAC 2018 Shared Task on Aggression Identification".
UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT.
Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books
Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow.
MPST: A Corpus of Movie Plot Synopses with Tags
In this paper, we set out to the task of collecting a corpus of movie plot synopses and tags.
RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
In this paper, we present our systems for the {``}SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News{''}.
