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The NLP Cypher | 11.22.20
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The NLP Cypher | 11.22.20

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Last Updated on July 24, 2023 by Editorial Team

Author(s): Ricky Costa

Originally published on Towards AI.

Botticelli

NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER

The NLP Cypher U+007C 11.22.20

Ultima Ratio Regum

Hey welcome back! EMNLP happened this week U+1F440. Tons of research came out and this newsletter won’t do justice to all of the great research conducted by institutions worldwide. But first…

We will be releasing an update to the Big Bad NLP Database this week and also a large update to the Super Duper NLP Repo after Thanksgiving. These updates will be delivered via our email NL, if interested, you can sign-up on our homepage.

As always, if you enjoy this read, please give it a U+1F44FU+1F44F and share with your enemies. U+1F601

Ok, knowledge graphs time: Once again, Michael Galkin released his incredibly detailed round-up newsletter U+1F525U+1F525. After a strong start in 2019 for knowledge augmented language models, it seems they continue to be the hot ticket for this year. Below is the TOC and link to full blog post (*warning* its extensive and awesome):

ToC

  1. KG-Augmented Language Models: Empower your Transformer
    1.1 Autoencoders
    1.2 Autoregressive
  2. Natural Language Generation: New Folks in Datasetlandia
  3. Entity Linking: Massive and Multilingual
  4. Relation Extraction: OpenIE 6 and Neural Extractors
  5. KG Representation Learning: Temporal KGC and Successor to FB15K-237
  6. ConvAI + KGs: On the Shoulders of OpenDialKG
  7. Wrapping Up

Knowledge Graphs in NLP @ EMNLP 2020

Your guide to the KG-related research in NLP, November edition.

mgalkin.medium.com

High Performance NLP at EMNLP (SLIDES)

Slides from Google and Uni. of Washington that explores the current state of scaling NLP Models in order to deal with large volumes text, cost and software and hardware considerations. This tutorial discusses the current and possible future directions for attacking these key areas for improving NLP efficiency.

GraphGym

An awesome library that recently came out and built on top of PyTorch Geometric. It allows for an easy configuration of data loading and can be easily initialized for various GNN configurations in parallel. This is a good library to start with if you feel Geometric on its own is too intimidating. U+1F60E

snap-stanford/GraphGym

GraphGym is a platform for designing and evaluating Graph Neural Networks (GNN). 1. Highly modularized pipeline for GNN…

github.com

Paper: https://arxiv.org/pdf/2011.08843.pdf

KeyBERT

This fella allows you to extract keywords and keyphrases from text by using BERT embeddings. It’s pretty straightforward and to conduct inference, you only need 3 lines of code. It’s fairly good, I tested it on abstract summaries from arXiv and I may use it to index the papers I read. U+270C

MaartenGr/KeyBERT

KeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and…

github.com

Linformer

Linformer is the “first theoretically proven linear-time transformer” out of FacebookAI (came out this Summer). In a nut shell, the amount of compute grows linearly with the amount of input length, unlike your typical transformer. U+1F447

This is great news for practitioners as this would allow one to really scale models in production, especially if you have to do millions of computations in a short amount of time. Apparently FB already has it running in production.

How Facebook uses super-efficient AI models to detect hate speech

Building AI that can analyze complicated text isn't enough to protect people from harmful content. We need systems that…

ai.facebook.com

Legal Search Engine

judyrecords is the largest search engine of United States court cases on the Internet.

Although the search engine boasts a huge catalogue, not all court documents can be made available online as some documents can only be requested in person at specific court houses. Still an awesome feat. When is the API coming out? U+1F601

judyrecords

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www.judyrecords.com

Podcast Search Engine

Interested in keeping up with podcasts: Here’s an API that allows you to search meta data of podcasts and episodes by people, places, or topics. The API is free as long as you stay within 2,500 requests per month.

Podcast API: Podcast Search & Directory API

We have a transparent and simple pricing model for Listen API. You can start with FREE plan without entering your…

www.listennotes.com

Tabular Transformers

A great medium article highlighting how to wrap the multimodal transformers library on top of the transformers library for tabular data! Currently the library supports 3 models: BERT, DistilBERT and RoBERTa. For training, you can use the trainer class from the Transformers lib.

Documentation: https://multimodal-toolkit.readthedocs.io/en/latest/modules/model.html#module-multimodal_transformers.model.tabular_transformers

Blog:

How to Incorporate Tabular Data with HuggingFace Transformers

[Colab] [Github]

medium.com

youtube-dl returns

Devs put up a good fight. It’s back?

GitHub explanation for reinstating youtube-dl repo:

Standing up for developers: youtube-dl is back – The GitHub Blog

Today we reinstated additional information youtube-dl, a popular project on GitHub, after we received about the project…

github.blog

Systematic Comparison of Open Information Extraction Techniques

In this paper from EMNLP, authors evaluated current deep learning systems for conducting open information extraction (OIE). That is, to automatically extract triplets from text so you can obtain subject predicate object from sentences. They explored different training scenarios for OIE, and compared existing OIE models. Good introductory paper if you are new to this space.

Paper: https://www.aclweb.org/anthology/2020.emnlp-main.690.pdf

Repo Cypher U+1F468‍U+1F4BB

A collection of recent released repos that caught our U+1F441

DiagNNose

This library contains a set of modules that can be used to analyze the activations of neural networks, with a focus on NLP architectures such as LSTMs and Transformers

i-machine-think/diagNNose

Paper: https://arxiv.org/abs/2011.06819 Demo: Documentation: https://diagnnose.readthedocs.io This library contains a…

github.com

NLPGym

NLPGym is a toolkit to bridge the gap between applications of RL and NLP. This aims at facilitating research and benchmarking of DRL application on natural language processing tasks.

rajcscw/nlp-gym

NLPGym is a toolkit to bridge the gap between applications of RL and NLP. This aims at facilitating research and…

github.com

Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian

Datasets and models for information extraction tasks in the Russian

iis-research-team/ner-rc-russian

Contribute to iis-research-team/ner-rc-russian development by creating an account on GitHub.

github.com

WikiAsp

WikiAsp is a multi-domain, aspect-based summarization dataset in the encyclopedic domain. In this task, models are asked to summarize cited reference documents of a Wikipedia article into aspect-based summaries.

neulab/wikiasp

This repository contains the dataset from the paper " WikiAsp: A Dataset for Multi-domain Aspect-based Summarization"…

github.com

Dataset of the Week: GrailQA

What is it?

Dataset used for knowledge base question answering (KBQA) containing 64,331 crowdsourced questions involving up to 4 relations and functions like counting, comparatives, and superlatives. The dataset covers all of the 86 domains in Freebase Commons.

Sample

Where is it?

Strongly Generalizable Question Answering Dataset

Strongly Generalizable Question Answering Dataset (GrailQA) is a new large-scale, high-quality dataset for question…

dki-lab.github.io

Paper: https://arxiv.org/pdf/2011.07743.pdf

Every Sunday we do a weekly round-up of NLP news and code drops from researchers around the world.

For complete coverage, follow our Twitter: @Quantum_Stat

Quantum Stat

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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',*/ ]; var replaceText = { '': '', '': '', '
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