Search Results for author:
Found 7 papers, 5 papers with code
Native Language Identification with Large Language Models
We present the first experiments on Native Language Identification (NLI) using LLMs such as GPT-4.
Why So Down? The Role of Negative (and Positive) Pointwise Mutual Information in Distributional Semantics
In distributional semantics, the pointwise mutual information ($\mathit{PMI}$) weighting of the cooccurrence matrix performs far better than raw counts.
Think Again Networks and the Delta Loss
This short paper introduces an abstraction called Think Again Networks (ThinkNet) which can be applied to any state-dependent function (such as a recurrent neural network).
Incorporating Subword Information into Matrix Factorization Word Embeddings
The positive effect of adding subword information to word embeddings has been demonstrated for predictive models.
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality
Increasing the capacity of recurrent neural networks (RNN) usually involves augmenting the size of the hidden layer, with significant increase of computational cost.
Enhancing the LexVec Distributed Word Representation Model Using Positional Contexts and External Memory
The effectiveness of both modifications is shown using word similarity and analogy tasks.
Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations
In this paper, we propose LexVec, a new method for generating distributed word representations that uses low-rank, weighted factorization of the Positive Point-wise Mutual Information matrix via stochastic gradient descent, employing a weighting scheme that assigns heavier penalties for errors on frequent co-occurrences while still accounting for negative co-occurrence.
