Semanlink - Favoris AND Dev

> Toolformer, **a model
trained to decide which APIs to call, when to
call them, what arguments to pass, and how to
best incorporate the results into future token
prediction**.

> fulfills the
following desiderata:
> - The use of tools should be learned in a
self-supervised way without requiring large
amounts of human annotations
>- The LM should be able to decide for itself when
and how to use which tool.

> Approach based
on the recent idea of using large LMs with incontext
learning (Brown et al., 2020) to generate
entire datasets from scratch.
>
> Given just a handful of human-written examples
of how an API can be used, 
> - we let a LM annotate
a huge language modeling dataset with potential
API calls. 
> - We then use a self-supervised loss to
determine which of these API calls actually help
the model in predicting future tokens. 
>- Finally, we
finetune the LM itself on the API calls that it considers
useful.

[Jay Hack @mathemagic1an sur twitter](https://twitter.com/mathemagic1an/status/1624870248221663232):

> from a small seed set of human inputs (essentially demonstrating usage of APIs), the training set for this behavior is generated by the LLM itself.
> 
> So what does this mean? We've found a promising way to tightly integrate arbitrary APIs with our best-performing models.

2023-02-13 A propos

Jay Hack sur Twitter : "My thoughts on Toolformer IMO the most important paper in the past few weeks..."

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2023-02-13 A propos

Timo Schick sur Twitter : "Introducing the Toolformer, a language model that teaches itself to use various tools in a self-supervised way..."

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2023-02-13 A propos

explosion/prodigy-openai-recipes: ✨ Bootstrap annotation with zero- & few-shot learning via OpenAI GPT-3

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2023-02-11 A propos

Towards a Tagalog NLP pipeline

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2023-02-04 A propos

deepset sur Twitter : " Generative models have taken the world of NLP by storm. But LLMs do not know about your personal data. This makes personal assistants, enterprise knowledge management and many other applications challenging. Retrieval augmented pipelines are the answer"

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2023-02-02 A propos

Jaided AI: EasyOCR

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2023-01-30 A propos

GitHub Copilot · Your AI pair programmer

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2023-01-28 A propos

Transformers-Tutorials/LayoutLMv3 at master · NielsRogge/Transformers-Tutorials

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2023-01-17 A propos

[Tutorial] How to Train LayoutLM on a Custom Dataset with Hugging Face

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2023-01-09 A propos

Where does hugging face's transformers save models? - Stack Overflow

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2023-01-04 A propos

Dan Shipper 📧 sur Twitter : "I spent Christmas programming with ChatGPT. Some observations:"

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2023-01-03 A propos

IBM/zshot: Zero and Few shot named entity & relationships recognition

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2022-12-23 A propos

LayoutLM Explained

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2022-12-21 A propos

Matthew Honnibal sur Twitter : "We've been working on new prodi.gy workflows that let you use the @OpenAI API to kickstart your annotations, via zero- or few-shot learning. ..."

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2022-12-20 A propos

4. Using the Snorkel-Labeled Dataset for Text Classification - Practical Weak Supervision [Book]

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2022-12-05 A propos

Alex sur Twitter : "Semantic search: how you can leverage both @CohereAI and @pinecone libraries to quickly build a POC..."

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2022-11-08 A propos

cardiffnlp/tweetnlp: "TweetNLP for all the NLP enthusiasts working on Twitter!..."

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2022-10-27 A propos

Harrison Chase sur Twitter : "Introducing LangChain: a python package aimed at helping build LLM applications through composability..."

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2022-10-25 A propos

Tutorial on Uncertainty Estimation for NLP

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2022-10-18 A propos

How to Train an mT5 Model for Translation With Simple Transformers | by Thilina Rajapakse | Towards Data Science

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2022-09-25 A propos

merve sur Twitter : "@huggingface transformers includes a new pipeline called Document Question Answering. This is a pipeline you can use to extract information from PDFs!...

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2022-09-20 A propos

Prompt Tuning BERT🎯:CommonLit Readability | Kaggle

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2022-09-16 A propos

Uncertainty Estimation for Natural Language Processing – Google Research

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2022-09-07 A propos

Active Learning with AutoNLP and Prodigy

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2022-09-06 A propos

A framework for designing document processing solutions

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2022-09-02 A propos

Philip Vollet sur Twitter : "Extracting information from PDFs or scanned documents is still a challenge! Use the @huggingface LayoutLMv3 model and Prodigy..."

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2022-09-02 A propos

Materials for ACL-2022 tutorial: Knowledge-Augmented Methods for NLP

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2022-08-26 A propos

AllenNLP sur Twitter : "Dataset: training data for @MetaAI 's No Language Left Behind NLLB-200 models!..."

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2022-08-25 A propos

Karl Higley sur Twitter : "Many ANN search tools (e.g. FAISS, ScaNN) allow you to provide multiple points as part of the same query..."

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2022-08-20 A propos

Connor Shorten sur Twitter : "Wow, incredible to see this from NASA! 🚀 Weaviate's integration of Vector Search with Graph Data and Symbolic Relations is a very interesting combination of technologies!..."

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2022-08-11 A propos

[2006.01969] REL: An Entity Linker Standing on the Shoulders of Giants

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2022-07-12 A propos

Cohere

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2022-07-08 A propos

Understanding Semantic Search and Question Answering | deepset

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2022-06-03 A propos

jboynyc/textnets: Text analysis with networks.

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2022-05-30 A propos

Max Irwin sur Twitter : "Instant Neural Search for your website! ..."

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2022-05-19 A propos

DataBorg - Knowledge management simplified

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2022-05-14 A propos

Sentence Embedding Fine-tuning for the French Language | by La Javaness R&D | Feb, 2022 | Medium

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2022-03-31 A propos

GuideToTransformersDomainAdaptation.ipynb - Colaboratory

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2022-03-18 A propos

Retraining roberta-base using the RoBERTa MLM Procedure | Medium

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2022-03-18 A propos

SPIKE for Knowledge Base Construction

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2022-02-22 A propos

Yosi Shamay sur Twitter : "a new platform for rapid ad-hoc knowledgebase construction using extractive search...."

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2022-02-22 A propos

Nils Reimers sur Twitter : "Creating intent classes for chatbots is challenging This tutorial shows how to use sentence-transformers to find potentially overlapping intent classes and how to improve your data annotation work." / Twitter

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2022-02-19 A propos

How to Build a Semantic Search Engine With Transformers and Faiss | by Kostas Stathoulopoulos | Towards Data Science

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2022-01-29 A propos

Modern Question Answering Systems Explained

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Haystack

2022-01-27 A propos

Haystack Annotation Tool

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Haystack Annotation Tool

2022-01-27 A propos

allenai/macaw: Multi-angle c(q)uestion answering

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2022-01-22 A propos

Integrate ORQA and REALM for Open Domain Question Answering · Issue #312 · deepset-ai/haystack

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2022-01-12 A propos

How to build a chatbot that reads all your data to find the right answer - Xatkit

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2022-01-11 A propos

Explosion 💥 sur Twitter : "...annotation tool and AutoNLP to train state-of-the-art NLP models!"

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2021-12-30 A propos

Multi-domain Multilingual Question Answering

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2021-12-08 A propos

semi-technologies/weaviate: Weaviate is a cloud-native, modular, real-time vector search engine

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2021-12-05 A propos

Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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2021-12-05 A propos

How to Fine-Tune Sentence-BERT for Question Answering | Capital One

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2021-11-21 A propos

Tutorial: Training AI bots to chat using MTurk and Facebook’s ParlAI | by Amazon Mechanical Turk | Happenings at MTurk

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2021-11-19 A propos

Building a sentence embedding index with fastText and BM25 | by David Mezzetti | Towards Data Science

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2021-09-30 A propos

neuml/txtai: Build AI-powered semantic search applications

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txtai

2021-09-30 A propos

Haystack (deepset)

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2021-09-20 A propos

Build NLP features into your product | deepset

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2021-09-20 A propos

stanfordnlp/stanza: Official Stanford NLP Python Library for Many Human Languages

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2021-09-20 A propos

How I almost won an NLP competition without knowing any Machine Learning - DEV Community

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2021-08-11 A propos

Nils Reimers sur Twitter : "How to train state-of-the-art sentence embeddings?"

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2021-06-22 A propos

librAIry

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2021-06-22 A propos

Transformer models - Hugging Face Course

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2021-06-15 A propos

Semantic Search with S-BERT is all you need

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2021-06-05 A propos

Improving Quality of Search Results Clustering with Approximate Matrix Factorisations (2006)

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Lingo

2021-06-02 A propos

Carrot2 search results clustering engine (online)

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Carrot2

2021-05-27 A propos

Transformers Pipelines.ipynb - Colaboratory

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2021-05-26 A propos

Rodrigo Nogueira sur Twitter : "Slides of our WSDM 2021 tutorial "Pretrained Transformers for Text Ranking: BERT and Beyond"

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2021-03-09 A propos

Why we switched from Spacy to Flair to anonymize French case law | by Michaël Benesty | Towards Data Science

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2021-02-10 A propos

[1911.03876] Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering

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2021-02-08 A propos

NLP Based Information Retrieval System | by Nikhil Sharma | Towards Data Science

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2021-01-27 A propos

Weaviate

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2021-01-18 A propos

pemistahl/lingua: natural language detection library for Java suitable for long and short text alike

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2020-12-12 A propos

FP Servant sur Twitter : "constructing a personal knowledge graph as a support for learning (and a metaphor of the learning experience)..."

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2020-12-03 A propos

Salmon Run: Word Sense Disambiguation using BERT as a Language Model

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2020-12-01 A propos

[2004.10964] Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

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2020-12-01 A propos

[2011.06993] FLERT: Document-Level Features for Named Entity Recognition

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2020-12-01 A propos

Semantic Scholar | AI-Powered Research Tool

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2020-11-24 A propos

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 A propos

[2010.05234] A Practical Guide to Graph Neural Networks

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2020-10-15 A propos

Wikifier

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2020-10-11 A propos

TAGME: on-the-fly annotation of short text fragments!

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2020-10-11 A propos

Which flavor of BERT should you use for your QA task? | by Olesya Bondarenko | Towards Data Science

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2020-10-04 A propos

Hugging Face sur Twitter : "No labeled data? No problem. The 🤗 Transformers master branch now includes a built-in pipeline for zero-shot text classification...

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2020-08-12 A propos

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2020-07-10 A propos

BERT Word Embeddings Tutorial · Chris McCormick

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2020-07-06 A propos

dicksontsai/stanford-nlp-local-extension: Chrome extension for sending content to localhost server running Stanford NLP tools.

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2020-07-03 A propos

Denny Britz sur Twitter : "I built a little frontend for my AI/ML papergraph tool..."

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2020-05-27 A propos

20,000 Roam Tags with Spacy

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2020-05-15 A propos

AmbiverseNLU: A Natural Language Understanding suite by Max Planck Institute for Informatics

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2020-03-13 A propos

FastHugs | ntentional

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2020-02-19 A propos

Hugging Face: How to train a new language model from scratch using Transformers and Tokenizers

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2020-02-16 A propos

Distilling BERT models with spaCy - Towards Data Science (2019)

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2020-02-15 A propos

Hugging Face sur Twitter : DistilBERT-cased for Question Answering w/ just 3 lines of javascript

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2020-02-14 A propos

Extractive Text Summarization Using spaCy in Python

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2020-02-09 A propos

Siamese Network for Image and Text similarity using Keras

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2020-01-22 A propos

Your own blog with GitHub Pages and fast_template (4 part tutorial) · fast.ai

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2020-01-21 A propos

GitHub - OpenNMT/OpenNMT-py: Open Source Neural Machine Translation in PyTorch

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2020-01-17 A propos

fastai/nbdev: Create delightful python projects using Jupyter Notebooks

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2020-01-12 A propos

Building a Search Engine with BERT and TensorFlow - Towards Data Science

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2020-01-12 A propos

Building a real-time embeddings similarity matching system | Solutions | Google Cloud

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2020-01-11 A propos

[1711.00046] Replace or Retrieve Keywords In Documents at Scale

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2020-01-09 A propos

Named Entity Recognition with Bert – Depends on the definition

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2020-01-09 A propos

Joint Intent Classification and Slot Filling with Transformers (Jupyter Notebook Viewer)

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2020-01-09 A propos

How to build deep neural network for custom NER with Keras

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2020-01-07 A propos

[1912.08904] Macaw: An Extensible Conversational Information Seeking Platform

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2020-01-01 A propos

Custom Named Entity Recognition Using spaCy - Towards Data Science

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2019-12-31 A propos

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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2019-12-11 A propos - Copie locale

Elasticsearch RSS feed indexer with Spacy entity extraction

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2019-11-24 A propos

Graph Neural Networks for Natural Language Processing tutorial at EMNLP 2019

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2019-11-08 A propos

Document analysis with machine learning

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2019-10-28 A propos

Sebastian Ruder sur Twitter : "It's great to see the growing landscape of NLP transfer learning libraries"

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2019-09-13 A propos

Blackstone Concept Extractor — ICLR&D

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2019-08-21 A propos

Make Delegation Work in Python · fast.ai

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2019-08-07 A propos

Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019) - Slides

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2019-07-13 A propos

Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019) - YouTube

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2019-07-13 A propos

SpacyIRL 2019 Conference in Overview | LinkedIn

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spaCy

2019-07-13 A propos

spaCy sur Twitter : "THE VIDEOS FROM #spaCyIRL ARE NOW LIVE!"

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spaCy

2019-07-12 A propos

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2019-07-02 A propos

Pipelines and composite estimators / ColumnTransformer for heterogeneous data — scikit-learn documentation

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2019-07-02 A propos

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 A propos

[1812.05944] A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Experiments

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2019-06-18 A propos

Transfer Learning in Natural Language Processing - Google Slides

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2019-06-04 A propos

State-of-the-art neural coreference resolution for chatbots

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2019-05-28 A propos

A Technique for Building NLP Classifiers Efficiently with Transfer Learning and Weak Supervision

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2019-05-22 A propos

Sebastian Ruder sur Twitter : "Besides the obvious things (ELMo, BERT, etc.), is there anything that we should definitely discuss at the NAACL "Transfer Learning in NLP" tutorial?"

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2019-05-20 A propos

Dandelion API - Semantic Text Analytics as a service

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2019-04-24 A propos

Introduction to PyTorch Code Examples

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2019-04-03 A propos

Sent2Vec: An unsupervised approach towards learning sentence embeddings | RARE Technologies

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2019-03-25 A propos

huggingface/pytorch-pretrained-BERT: The Big-&-Extending-Repository-of-Transformers: Pretrained PyTorch models for Google's BERT, OpenAI GPT & GPT-2, Google/CMU Transformer-XL.

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2019-03-15 A propos

Measuring patent claim breadth using Google Patents Public Datasets | Google Cloud Blog

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2019-02-21 A propos

Plotting Similar Patents | Kaggle

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2019-02-19 A propos

Using BERT for state-of-the-art pre-training for natural language processing

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2019-02-14 A propos

Keywords2vec

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2019-02-09 A propos

Jeremy Howard on Twitter: "Such a ridiculously simple idea couldn't possibly work, could it? Or... could it? "

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2019-02-09 A propos

nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

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2019-02-07 A propos

A collection of notebooks for Natural Language Processing from NLP Town

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2019-02-07 A propos

BPEmb: Subword Embeddings

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2019-01-31 A propos

StanfordNLP | StanfordNLP

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2019-01-30 A propos

Cheatsheet · fastText

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2019-01-29 A propos

What is torch.nn really? — PyTorch Tutorials 1.0.0

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2019-01-16 A propos

Open-sourcing PyText for faster NLP development

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2018-12-15 A propos

Combining numerical and text features in (deep) neural networks - Digital Thinking

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2018-12-12 A propos

Prodigy · An annotation tool for AI, Machine Learning & NLP

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2018-12-09 A propos

Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python - Analytics Vidhya

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2018-12-01 A propos

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2018-11-25 A propos

Matthew Honnibal sur Twitter : "Have been experimenting with an unsupervised pre-training technique for @spacy_io, similar to ULMFit/Elmo/BERT etc.

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2018-11-18 A propos

iliaschalkidis/ELMo-keras: Re-implementation of ELMo on Keras

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2018-11-14 A propos

Deep Chit-Chat: deep learning for chatbots (EMNLP 2018 Tutorial)

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by Dr Wei Wu (Microsoft Xiaolce - chatbot with 200 millions users in China) and Dr Rui Yan (Peking Univ)

- Chit-chat (casual, non goal oriented) open-domain. Must be relevant to the context and diverse (informative) to be engaging.
- why creating a chat? to prove an AI can speak like a human, commercial reasons, link to services.

Task oriented vs non task oriented: this tutorial is about the second one.

Retrieval based vs generation based.

Basic knowledge of DL for chatbots:

- word embeddings
- sentence embeddings (CNN, RNN)
- dialogue modeling: seq-to-seq with attention

Response selection for retrieval based chatbots:

- single turn response selection (slides 37-57)
    - framework 1: matching with seq embeddings
    - framework 2: matching with message-response interaction (46)
    - extension of 1: KnowledgeMatching with External Knowledge (53)
    - extension of 2: RepresentationsMatching with Multiple Levels of Representations (54)
    - insights from comparison between 1 and 2 (57)
- multi turn response selection (62)
    - context is now: mess + history
    - again, 2 frameworks

Emerging directions (79):

- matching with better representations
    - Self-Attention (82)
    - fusing multiple types of repr. But how to fuse matters (83)
    - pre-training

Learning a matching model for response selection (84)

Generation based models for chatbots:

- single turn generarion (89)
    - Basic generation model
        - seq2seq
        - Attention
        - Bi-directional modeling
- multi turn generation
    - Contexts are important
    - Context sensitive models
    - Hierarchical context modeling
    - Latent variable modeling
    - Hierarchical memory networks

Diversity in conversations (99)

Content introducing (106)

Additional elements (113)

- Topics in cnversation
- Emotions

Persona in chat:

- Persona
- ...
- Knowledge
- Common sense

RL and Adversarial learning in conversations (125)

Evaluation (132)

Future trends:

- Reasoning in dialogues
- X-grounded dialogues

2018-11-06 A propos

Joint Models in NLP - Slides - Tutorial (EMNLP 2018) - Yue Zhang

Tags:

**Joint models: solve 2 tasks at once.**

Related tasks: POS tagging, NER, chuncking. Pipeline tasks

Motivations:

- reduce error propagation
- information exchange between tasks

Challenges:

- Joint learning
- Search

History: statistical models. 2 kinds:

- Graph-Based Methods
    - Traditional solution:
        - Score each candidate, select the highest-scored output
        - Search-space typically exponential
- Transition-Based Methods
    - Transition-Based systems: Automata
        - State: partial result during decoding, Action: operations that can be applied for state transition
        - Output constructed incrementally

- Deep learning based model
    - Neural transition based models
    - Neural graph-based models
        - Cross task
            - Seminal work: Collobert, Ronan, et al. "Natural language processing (almost) from scratch."
            - Not all tasks are mutually beneficial
            - Ramachandran, et al.  “Unsupervised pretraining for sequence to sequence learning.”
            - Peters, Matthew E., et al. "Deep contextualized word representations." (ELMo)
            - "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding."
            - ULMFIT
            - Correlation between multi-task learning and pretraining
        - Cross lingual
        - Cross domain
        - Cross standard

2018-11-06 A propos

Writing code for Natural language processing Research

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2018-11-05 A propos

Cross-Lingual Word Representations: Induction and Evaluation (Tutorial EMNLP 2017)

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2018-11-05 A propos

Deep Latent-Variable Models for Natural Language - Tutorial - harvardnlp

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2018-11-01 A propos

Writing Code for NLP Research, AllenNLP's tutorial at #emnlp2018

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2018-10-31 A propos

Tutorials - EMNLP 2018

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2018-10-31 A propos

Practical Text Classification With Python and Keras – Real Python

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2018-10-25 A propos

The Annotated Transformer

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2018-10-12 A propos

Unsupervised Text Summarization using Sentence Embeddings

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2018-10-09 A propos

Neural Network Embeddings Explained – Towards Data Science

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2018-10-09 A propos

Key topics extraction and contextual sentiment of users’ reviews

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2018-09-18 A propos

Named Entity Recognition and Classification with Scikit-Learn

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2018-09-16 A propos

Depends on the definition - it's about machine learning, data science and more

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2018-09-09 A propos

zalandoresearch/flair: A very simple framework for state-of-the-art NLP

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2018-08-24 A propos

Practical guide to text classification | Google Developers

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2018-07-23 A propos

Natural Language Processing is Fun! – Adam Geitgey – Medium

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2018-07-23 A propos

SIGIR 2018 Tutorial - Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between

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2018-07-09 A propos

Module google/universal-sentence-encoder | TensorFlow

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2018-05-23 A propos

Serving a model with Flask

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2018-05-21 A propos

Testing Tensorflow code

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2018-05-21 A propos

How to Train your Own Model with NLTK and Stanford NER Tagger? (for English, French, German…)

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2018-05-20 A propos

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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2018-05-20 A propos

Implementing Poincaré Embeddings | RARE Technologies

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2018-05-20 A propos

NLP using Word Vectors with Spacy - CLDSPN | Kaggle

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2018-05-11 A propos

Text Classification with TensorFlow Estimators

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2018-04-17 A propos

Part-of-Speech tagging tutorial with the Keras Deep Learning library - Cdiscount TechBlog

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2018-04-13 A propos

Text Data Preprocessing: A Walkthrough in Python

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2018-04-09 A propos

Sense2vec with spaCy and Gensim · Blog · Explosion AI

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2018-04-08 A propos

A short introduction to NLP in Python with spaCy – Towards Data Science

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2018-04-05 A propos

ML-knn: A lazy learning approach to multi-label learning (2007)

Tags:

a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 A propos

GitHub - anvaka/word2vec-graph: Exploring word2vec embeddings as a graph of nearest neighbors

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2018-03-12 A propos

SPACY'S ENTITY RECOGNITION MODEL: incremental parsing with Bloom embeddings & residual CNNs - YouTube

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2018-03-06 A propos

Intro to text classification with Keras: automatically tagging Stack Overflow posts | Google Cloud Big Data and Machine Learning Blog

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2018-03-04 A propos

A Benchmark of Text Classification in PyTorch

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2018-02-28 A propos

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 A propos

[1801.01586] A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

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2018-01-09 A propos

gensim/WMD_tutorial.ipynb

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2017-12-23 A propos

Topic Modeling with Scikit Learn – Aneesha Bakharia – Medium

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2017-12-05 A propos

How to use pre-trained word vectors from Facebook’s fastText

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2017-11-25 A propos

Modifications for the Cluster Content Discovery and the Cluster Label Induction Phases of the Lingo Algorithm (2014)

Tags:

Lingo

2017-11-11 A propos

Implementing a CNN for Text Classification in TensorFlow – WildML

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2017-11-06 A propos

How to Write a Spelling Corrector (Peter Norvig)

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2017-10-25 A propos

Un correcteur orthographique en 21 lignes de Python

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2017-10-25 A propos

LSTM with word2vec embeddings | Kaggle

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2017-10-25 A propos

Using Gensim Word2Vec Embeddings in Keras | Ben Bolte's Blog

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2017-10-23 A propos

A Word2Vec Keras tutorial

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2017-10-23 A propos

Using pre-trained word embeddings in a Keras model

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2017-10-23 A propos

TensorFlow Neural Machine Translation (seq2seq) Tutorial

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2017-09-18 A propos

Concept Search on Wikipedia · Chris McCormick

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2017-09-10 A propos

Word2Vec Tutorial Part 2 - Negative Sampling · Chris McCormick

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2017-09-10 A propos

Word2Vec Tutorial - The Skip-Gram Model · Chris McCormick

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2017-09-10 A propos

Vector Representations of Words | TensorFlow

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2017-08-28 A propos

Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models | Blog | Explosion AI

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2017-07-20 A propos

Gensim tutorial: Similarity Queries

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2017-07-19 A propos

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2017-07-19 A propos

Watson: Alchemy Language v1 API Explorer

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2017-07-18 A propos

Dealing with Human Language | Elasticsearch: The Definitive Guide [master]

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2017-07-18 A propos

Dive Into NLTK, Part V: Using Stanford Text Analysis Tools in Python – Text Mining Online

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2017-07-11 A propos

NLTK: Installing Third Party Software · nltk Wiki

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2017-07-11 A propos

Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (slides)

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2017-07-11 A propos

Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (2004) (paper)

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2017-07-11 A propos

Source code for nltk.tag.stanford — NLTK documentation

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2017-07-11 A propos

RDRPOSTagger: A Rule-based Part-of-Speech and Morphological Tagging Toolkit

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2017-07-11 A propos

TreeTagger - a part-of-speech tagger for many languages

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2017-07-11 A propos

nltk.tag.stanford module — NLTK documentation

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2017-07-11 A propos

Stanford Log-linear Part-Of-Speech Tagger

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Stanford POS Tagger

2017-07-11 A propos

What are all possible pos tags of NLTK? - Stack Overflow

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2017-07-11 A propos

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2017-07-10 A propos

gensim: models.phrases – Phrase (collocation) detection

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2017-07-10 A propos

Teaching a Computer to Read: - Scripted

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2017-07-10 A propos

Cortical.io - Fast, precise, intuitive NLP

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Cortical.io

2017-07-10 A propos

spaCy - Industrial-strength Natural Language Processing in Python

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spaCy

2017-06-29 A propos

How can I tag and chunk French text using NLTK and Python? - Stack Overflow

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2017-06-29 A propos

TextBlob: Simplified Text Processing

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TextBlob

2017-06-29 A propos

Document Clustering with Python

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2017-06-28 A propos

gensim/FastText_Tutorial.ipynb

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2017-06-28 A propos

NLTK Book

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NLTK

2017-06-26 A propos

Salmon Run: Implementing the RAKE Algorithm with NLTK

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2017-06-26 A propos

RAKE: A python implementation of the Rapid Automatic Keyword Extraction

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2017-06-26 A propos

Gensim tutorials

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2017-06-23 A propos

Accessing Text Corpora and Lexical Resources

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2017-06-20 A propos

Recurrent Neural Networks with Word Embeddings — DeepLearning 0.1 documentation

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2017-06-16 A propos

Intro to Automatic Keyphrase Extraction

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2017-06-14 A propos

html2text

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2017-06-13 A propos

Automated Keyword Extraction – TF-IDF, RAKE, and TextRank (Less Than Dot - Blog)

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2017-06-13 A propos

An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec

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2017-06-09 A propos

word2vec-api

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2017-06-09 A propos

News classification with topic models in gensim

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2017-06-07 A propos

Lingo3G or Carrot2? | Carrot Search

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Carrot2

2017-06-04 A propos

Using Gensim for LDA (notebook)

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2017-06-02 A propos

(Relatively) quick and easy Gensim example code | William Bert

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2017-06-02 A propos

Text Summarization with Gensim

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2017-06-02 A propos

pyLDAvis

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2017-06-02 A propos

gensim: models.word2vec – Deep learning with word2vec

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2017-06-01 A propos

Word2vec in gensim Tutorial | RaRe Technologies

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2017-06-01 A propos

NLP keyword extraction tutorial with RAKE and Maui

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2 tools:

- simple keyword extraction with a Python library (RAKE)
- Java tool (Maui) that uses a machine-learning technique.

Focus on 2 tasks:

- Extracting the most significant words and phrases that appear in given text
- Identifying a set of topics from a predefined vocabulary that match a given text

Typical steps:

- Candidate selection (extract all possible words, phrases, terms or concepts that can potentially be keywords).
- Properties calculation (for each candidate, properties that indicate that it may be a keyword)
- Scoring and selecting keywords

RAKE: finding multi-word phrases containing frequent words. +: simplicity, ease of use -: limited accuracy, parameter configuration requirement, throws away many valid phrases, doesn’t normalize candidates (no stemming).

Maui: ("Multi-purpose automatic topic indexing"). Based on [Weka](/semanlink/tag/weka) (GPL, java, maven, github). Compared to RAKE:

- Extract keywords not just from text, but also with a reference to a controlled vocabulary
- Improve the accuracy by training Maui on manually chosen keywords
    - but requires a training model.

Maui can use a controlled vocabulary expressed in SKOS - so I could use it in semanlink!

2017-05-24 A propos

Swayy | Blog — An algorithm for generating automatic hashtags

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2017-05-24 A propos

An Efficient Way to Extract the Main Topics from a Sentence | The Tokenizer

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