Search Results for author:
Found 22 papers, 6 papers with code
Unsupervised document summarization using pre-trained sentence embeddings and graph centrality
This paper describes our submission for the LongSumm task in SDP 2021.
SNLP at TextGraphs 2022 Shared Task: Unsupervised Natural Language Premise Selection in Mathematical Texts Using Sentence-MPNet
This paper describes our system for the submission to the TextGraphs 2022 shared task at COLING 2022: Natural Language Premise Selection (NLPS) from mathematical texts.
VISLA Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations
This paper introduces the VISLA (Variance and Invariance to Semantic and Lexical Alterations) benchmark, designed to evaluate the semantic and lexical understanding of language models.
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
We present Stance Reasoner, an approach to zero-shot stance detection on social media that leverages explicit reasoning over background knowledge to guide the model's inference about the document's stance on a target.
Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights
Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights.
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT
Various complex methods have claimed to overcome this limit, but recent research questions the efficacy of these models across different classification tasks.
MPTopic: Improving topic modeling via Masked Permuted pre-training
Topic modeling is pivotal in discerning hidden semantic structures within texts, thereby generating meaningful descriptive keywords.
QuOTeS: Query-Oriented Technical Summarization
To address this problem, we propose QuOTeS, an interactive system designed to retrieve sentences related to a summary of the research from a collection of potential references and hence assist in the composition of new papers.
DimenFix: A novel meta-dimensionality reduction method for feature preservation
Dimensionality reduction has become an important research topic as demand for interpreting high-dimensional datasets has been increasing rapidly in recent years.
Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence Models
In this paper, we propose a novel method based on character sequence-to-sequence models to correct documents already processed with Optical Character Recognition (OCR) systems.
Optical Character Recognition
Optical Character Recognition (OCR)
Neural Abstractive Unsupervised Summarization of Online News Discussions
Our novel approach provides a summary that represents the most relevant aspects of a news item that users comment on, incorporating the social context as a source of information to summarize texts in online social networks.
Abstractive Text Summarization
Unsupervised Text Summarization
Active learning for medical code assignment
Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-making.
COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing
The COVID-19 pandemic has affected people's lives in many ways.
Detecting Ongoing Events Using Contextual Word and Sentence Embeddings
This paper introduces the Ongoing Event Detection (OED) task, which is a specific Event Detection task where the goal is to detect ongoing event mentions only, as opposed to historical, future, hypothetical, or other forms or events that are neither fresh nor current.
Enhancement of Short Text Clustering by Iterative Classification
Short text clustering is a challenging task due to the lack of signal contained in such short texts.
Vector Embedding of Wikipedia Concepts and Entities
Using deep learning for different machine learning tasks such as image classification and word embedding has recently gained many attentions.
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis
This paper introduces a new large-scale n-gram corpus that is created specifically from social media text.
