Vectorizing the Tree: High-Performance Word2Vec in Pure PyTorch
A production-grade implementation of Hierarchical Softmax and Negative Sampling, featuring vectorized tree traversals, …...
Natural Language Processing
Sarcasm Detection with Transformers: A Cautionary Tale
Learn how dataset bias can lead to misleading results in NLP: a sarcasm detection model that actually learned to …
Analytical Solution to Word2Vec Softmax & Corpus Bias Probing
Analytical derivation of Word2Vec's softmax objective factorization and a new framework for detecting semantic bias in …...
EigenNoise: Data-Free Word Vector Initialization
Investigation into EigenNoise, a data-free initialization scheme for word vectors that approaches pre-trained model …...
Data-Driven WordNet Construction from Wiktionary
We introduce an unsupervised algorithm for inducing semantic networks from noisy, crowd-sourced data, producing a …...
QuAC: Question Answering in Context Dataset
Analysis of QuAC's conversational QA through student-teacher interactions, featuring 100K+ context-dependent questions …
CoQA Dataset: Advancing Conversational Question Answering
Analysis of CoQA, a conversational QA dataset with multi-turn dialogue, coreference resolution, and natural answers for …
Count Vectorization with scikit-learn in Python
Learn count vectorization in Python: convert text to numerical vectors using scikit-learn's CountVectorizer with …
Word Embeddings in NLP: An Introduction
Learn about word embeddings in NLP: from basic one-hot encoding to contextual models like ELMo. Guide with examples.