Discovering Universal Geometry in Embeddings with ICA
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Updated
May 26, 2024 - Python
Discovering Universal Geometry in Embeddings with ICA
EMNLP 2023 Papers: Explore cutting-edge research from EMNLP 2023, the premier conference for advancing empirical methods in natural language processing. Stay updated on the latest in machine learning, deep learning, and natural language processing with code included. ⭐ support NLP!
The repository provides links to collections of influential and interesting research papers from top AI conferences, with open-source code to promote reproducibility and provide detailed implementation insights beyond the scope of the article. Stay up to date with the latest advances in AI research!
"Bootstrapping Relationship Extractors with Distributional Semantics" (Batista et al., 2015) in EMNLP'15 - Python implementation
Improving word mover’s distance by leveraging self-attention matrix
[EMNLP'21] Visual News: Benchmark and Challenges in News Image Captioning
A curated list of awesome sentiment analysis studies, in which attitude corresponds to the text position conveyed by Subject towards other Object mentioned in text such as: entities, events, etc.
[EMNLP 2022 Findings] Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
Compendium of the resources available from top NLP conferences.
This repository contains the dataset and codes for the task of the prediction of subframes in new text from the paper "Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media" (EMNLP'2020).
A Systematic Investigation of Transferability and Robustness of Humor Detection Models
[EMNLP 2023] Question Answering as Programming for Solving Time-Sensitive Questions
This repository contains codes for NLP project "In-context Learning of Pre-trained Language Models for Controlled Dialogue Summarization: A Holistic Benchmark and Empirical Analysis"
Research code and scripts used in the Silburt et al. (2021) EMNLP 2021 paper 'FANATIC: FAst Noise-Aware TopIc Clustering'
Code and data for paper "Dialog Intent Induction with Deep Multi-View Clustering", Hugh Perkins and Yi Yang, 2019, EMNLP 2019
Code to reproduce the results of the paper 'Towards Realistic Few-Shot Relation Extraction' (EMNLP 2021)
Research code and scripts used in the paper Semantic Role Labeling as Syntactic Dependency Parsing.
📜 [NLLP 2022] "Efficient Deep Learning-based Sentence Boundary Detection in Legal Text", Reshma Sheik and Gokul T. Adethya and Dr. S. Jaya Nirmala
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