h-tayyarmadabushi Goto Github PK
Name: Harish Tayyar Madabushi
Type: User
Company: The University of Bath
Bio: Lecturer in Artificial Intelligence, University of Bath
Twitter: harish
Location: Bath, UK
Name: Harish Tayyar Madabushi
Type: User
Company: The University of Bath
Bio: Lecturer in Artificial Intelligence, University of Bath
Twitter: harish
Location: Bath, UK
Article prediction is a task that has long defied accurate linguistic description. As such, this task is ideally suited to evaluate models on their ability to emulate native-speaker intuition. To this end, we compare the performance of native English speakers and pre-trained models on the task of article prediction set up as a three way choice (a/an, the, zero). Our experiments with BERT show that BERT outperforms humans on this task across all articles. In particular, BERT is far superior to humans at detecting the zero article, possibly because we insert them using rules that the deep neural model can easily pick up. More interestingly, we find that BERT tends to agree more with annotators than with the corpus when inter-annotator agreement is high but switches to agreeing more with the corpus as inter-annotator agreement drops. We contend that this alignment with annotators, despite being trained on the corpus, suggests that BERT is not memorising article use, but captures a high level generalisation of article use akin to human intuition.
Data and model outputs for the paper: Adjudicating LLMs as PropBank Annotators
Data and Baselines for AStitchInLanguageModels dataset
A Python package for learning, evaluating, annotating, and extracting vector representations of construction grammars
The Construction Grammar Schematicity (``CoGS'') corpus consists of 10 distinct English constructions, where the constructions vary with respect to schematicity.
Transformers for Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data
Construction Grammar based BERT
Emergent Abilities in Large Language Models just In-Context Learning
HTML::Miner - This Module 'Mines' (hopefully) useful information for an URL or HTML snippet.
BERT-NER (nert-bert) with google bert https://github.com/google-research.
Net::XMPP::Client::GTalk - This module provides an easy to use wrapper around the Net::XMPP class of modules for specific access to GTalk ( Both on Gmail and Google Apps ).
Data and preprocessing scripts for SemEval 2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
Multilingual Sentence & Image Embeddings with BERT
This (Perl) module provides access to PerlMonks.
A smart, easy and powerful way to access/create XML files/data (Perl).
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.