#PROJECT2020 #NLP365

Natural Language Processing 365 ✅

One NLP blog post per day for 365 days. 1 > 0

Data Science

Day 364: Ryan’s PhD Journey – OpenKE-PyTorch Library Analysis + code snippets for 11 KE models

OpenKE in PyTorch 11 KE models in PyTorch See above - the only one missing is HolE cause I haven't read the paper yet! 3…
Data Science

Day 363: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes XIV

Cross-lingual entity alignment via joint attribute-preserving embedding Proposed a joint attribute-preserving embedding model for cross-lingual entity alignment, where it has two main modules / embeddings…
Data Science

Day 362: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes XIII

Open knowledge enrichment for long-tail entities Proposed OKELE, an end-to-end method to enrich long-tail entities from the open Web! It's based on the idea that…
Data Science

Day 361: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes XII

Neural relation extraction via inner-sentence noise reduction and transfer learning Proposed a novel word-level distant supervision approach for relation extraction, which consists of two steps:…
Data Science
Day 360: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes XI
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Day 359: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes X
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Day 358: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes IX
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Day 357: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes VIII
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Day 356: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes VII
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Day 355: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes VI
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Day 354: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes V
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Day 353: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes IV
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Day 352: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes IIII
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Day 351: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes III
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Day 350: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes II
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Day 349: Ryan’s PhD Journey – Literature Review – Knowledge Acquisition – 1st Passes I
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Day 348: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes VIII
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Day 347: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes VII
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Day 346: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes VI
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Day 345: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes V
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Day 344: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes IV
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Day 343: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes III
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Day 342: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes II
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Day 341: Ryan’s PhD Journey – Literature Review – Knowledge Representation – 1st Passes I
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Day 340: Ryan’s PhD Journey – Literature Review – List of Future Work Related Papers
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Day 339: Ryan’s PhD Journey – Literature Review – List of Knowledge-aware Applications Papers
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Day 338: Ryan’s PhD Journey – Literature Review – List of Temporal Knowledge Graph Papers
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Day 337: Ryan’s PhD Journey – Literature Review – List of Relation Extraction Papers
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Day 336: Ryan’s PhD Journey – Literature Review – List of Entity Discovery Papers
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Day 335: Ryan’s PhD Journey – Literature Review – List of Knowledge Graph Completion Papers
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Day 334: Ryan’s PhD Journey – Literature Review – List of Encoding Models & Auxiliary Information Papers
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Day 333: Ryan’s PhD Journey – Literature Review – List of Scoring Functions Papers
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Day 332: Ryan’s PhD Journey – Literature Review – List of Deep Learning & Knowledge Graphs Papers
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Day 331: Ryan’s PhD Journey – Literature Review – List of Knowledge Graph Representation Papers
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Day 330: Ryan’s PhD Journey – Refinitiv Knowledge Graph Info
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Day 329: Ryan’s PhD Journey – Link Prediction – Traditional Pipeline
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Day 328: Ryan’s PhD Journey – Link Prediction – General architecture and Negative Sampling
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Day 327: Ryan’s PhD Journey – Link Prediction – Introduction
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Day 326: Ryan’s PhD Journey – Nodes 2020 Notes II
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Day 325: Ryan’s PhD Journey – Nodes 2020 Notes I
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Day 324: Ryan’s PhD Journey – Applications and Future Work of GNNs
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Day 323: Ryan’s PhD Journey – Variants of GNNs – Training Methods and General Frameworks
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Day 322: Ryan’s PhD Journey – Variants of GNNs – Propagation Step
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Day 321: Ryan’s PhD Journey – Variants of GNNs – Graph Types
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Day 320: Ryan’s PhD Journey – Introduction to GNNs
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Day 319: Ryan’s PhD Journey – Overview of Graph Neural Networks
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Day 318: Ryan’s PhD Journey – Future Directions in KGs
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Day 317: Ryan’s PhD Journey – Temporal Knowledge Graph & Knowledge-Aware Applications
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Day 316: Ryan’s PhD Journey – Knowledge Acquisition II
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Day 315: Ryan’s PhD Journey – Knowledge Acquisition I
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Day 314: Ryan’s PhD Journey – Knowledge Representation Learning III
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Day 313: Ryan’s PhD Journey – Knowledge Representation Learning II
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Day 312: Ryan’s PhD Journey – Knowledge Representation Learning I
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Day 311: Ryan’s PhD Journey – Overview of Knowledge Graphs
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Day 310: NLP Discovery – DiffBot’s Knowledge Graph API
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Day 309: Ryan’s PhD Journey – From Documents to Graph
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Day 308: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 3 – Entity Resolution III
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Day 307: Ryan’s PhD Journey – Neo4j’s Python Driver – How to connecting Python with Neo4j
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Day 306: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 3 – Entity Resolution II
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Day 305: Ryan’s PhD Journey – Why Graph Databases (Neo4j)
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Day 304: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 3 – Entity Resolution I
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Day 303: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 2 – Information Extraction V
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Day 302: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 2 – Information Extraction IV
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Day 301: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 2 – Information Extraction III
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Day 300: Ryan’s PhD Journey – Cypher’s Recommendation Engine Tutorial
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Day 299: Ryan’s PhD Journey – Cypher’s Hello World – Movie Graph Tutorial II
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Day 298: Ryan’s PhD Journey – Cypher’s Hello World – Movie Graph Tutorial I
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Day 297: Ryan’s PhD Journey – Cypher’s User Defined Procedures and Functions
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Day 296: Ryan’s PhD Journey – Cypher’s Datetimes and Subqueries
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Day 295: Ryan’s PhD Journey – Cypher’s Controlling Query Processing
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Day 294: Ryan’s PhD Journey – Cypher’s Filtering Query Results
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Day 293: Ryan’s PhD Journey – Cypher’s CRUD Operations
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Day 292: Ryan’s PhD Journey – Cypher’s Queries and Patterns
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Day 291: Learn NLP With Me – Named-Entity (NER) evaluation metrics based on entity-level
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Day 290: Ryan’s PhD Journey – Cypher Introduction
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Day 289: Ryan’s PhD Journey – Neo4j Graph Fundamentals
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Day 288: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 2 – Information Extraction II
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Day 287: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 2 – Information Extraction I
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Day 286: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 1 – What Is a Knowledge Graph II
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Day 285: Learn NLP With Me – Domain-Specific KG Textbook – Chapter 1 – What Is a Knowledge Graph I
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Day 284: Learn NLP With Me – Introduction to Flair for NLP
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Day 283: Learn NLP With Me – Hidden Markov Models (HMMs) III
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Day 282: Learn NLP With Me – Building an Enterprise Knowledge Graph at Uber
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Day 281: NLP Papers Summary – Knowledge Reasoning over Knowledge Graph I
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Day 280: NLP Discovery – lang.ai’s Unsupervised Intent Discovery (Whitepaper)
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Day 279: Learn NLP With Me – Trustworthy and Explainable AI Achieved Through Knowledge Graphs
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Day 278: Learn NLP With Me – Richer Sentence Embeddings using Sentence-BERT
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Day 277: Learn NLP With Me – Using Knowledge Graphs to Identify Investment Opportunities
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Day 276: Learn NLP With Me – Knowledge Graph for Financial Services
Ryan's PhD Journey
Day 275: Ryan’s PhD Journey – The Beginning of a New Chapter – Starting with Why
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Day 274: Learn NLP With Me – Training the named entity recognizer using SpaCy III
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Day 273: Learn NLP With Me – Hidden Markov Models (HMMs) II
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Day 272: NLP Discovery – Prodigy Annotation Tool
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Day 271: Learn NLP With Me – Hidden Markov Models (HMMs) I
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Day 270: Learn NLP With Me – Training the named entity recognizer using SpaCy II
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Day 269: Learn NLP With Me – Training the named entity recognizer using SpaCy I
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Day 268: Learn NLP With Me – Building a Conversational Interface III
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Day 267: Learn NLP With Me – Building a Conversational Interface II
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Day 266: Learn NLP With Me – Building a Conversational Interface I
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Day 265: Learn NLP With Me – Intent Classification for Chatbots (Airbnb’s Approach)
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Day 264: Learn NLP With Me – SLP Textbook Ch.26 – Dialogue Systems and Chatbots VI
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Day 263: Learn NLP With Me – SLP Textbook Ch.26 – Dialogue Systems and Chatbots V
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Day 262: Learn NLP With Me – SLP Textbook Ch.26 – Dialogue Systems and Chatbots IV
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Day 261: Learn NLP With Me – SLP Textbook Ch.26 – Dialogue Systems and Chatbots III
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Day 260: Learn NLP With Me – SLP Textbook Ch.26 – Dialogue Systems and Chatbots II
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Day 259: Learn NLP With Me – SLP Textbook Ch.26 – Dialogue Systems and Chatbots I
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Day 258: Learn NLP With Me – SLP Textbook Ch.23 – Discourse Coherence V
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Day 257: Learn NLP With Me – SLP Textbook Ch.23 – Discourse Coherence IV
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Day 256: Learn NLP With Me – SLP Textbook Ch.23 – Discourse Coherence III
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Day 255: Learn NLP With Me – SLP Textbook Ch.23 – Discourse Coherence II
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Day 254: Learn NLP With Me – SLP Textbook Ch.23 – Discourse Coherence I
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Day 253: Learn NLP With Me – CS520 Knowledge Graphs – Lecture 5 – How to evolve a knowledge graph?
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Day 252: Learn NLP With Me – CS520 Knowledge Graphs – Lecture 4 – What are some knowledge graph inference algorithms?
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Day 251: Learn NLP With Me – CS520 Knowledge Graphs – Lecture 3 – What are some advanced knowledge graphs?
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Day 250: Learn NLP With Me – CS520 Knowledge Graphs – Lecture 2 – How to create a knowledge graph?
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Day 249: Learn NLP With Me – CS520 Knowledge Graphs – Lecture 1 – What is a knowledge graph?
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Day 248: NLP Implementation – A Simple Knowledge Graph Walkthrough
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Day 247: NLP Implementation – A Web Application for Entity Tracking – React Frontend
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Day 246: NLP Implementation – A Web Application for Entity Tracking – Flask Backend
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Day 245: NLP Implementation – News Article Ingestion Pipeline – Putting it All Together
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Day 244: NLP Implementation – Entity Extraction and Linking – Entity Linking using DBPedia
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Day 243: NLP Implementation – Entity Extraction and Linking – NER and Coreference Resolution using SpaCy
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Day 242: NLP Implementation – Topic Modelling and Sentiment Analysis on News Articles (Sentence Level)
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Day 241: NLP Implementation – Topic Modelling and Sentiment Analysis on News Articles (Document Level)
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Day 240: NLP Implementation – Kaggle’s Fake News Challenge – BERT Classifier using PyTorch and HuggingFace III
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Day 239: NLP Implementation – Kaggle’s Fake News Challenge – BERT Classifier using PyTorch and HuggingFace II
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Day 238: NLP Implementation – Kaggle’s Fake News Challenge – BERT Classifier using PyTorch and HuggingFace I
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Day 237: Learn NLP With Me – An Exhaustive Guide to Detecting and Fighting Neural Fake News using NLP
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Day 236: NLP Papers Summary – A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts
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Day 235: Learn NLP With Me – Topic Modelling with LSA and LDA
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Day 234: NLP Papers Summary – Topic Modeling in Financial Documents
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Day 233: Learn NLP With Me – LinkedIn’s Knowledge Graph
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Day 232: NLP Papers Summary – Building and Exploring an EKG for Investment Analysis – Deployment and Related Work
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Day 231: NLP Papers Summary – Building and Exploring an EKG for Investment Analysis – Building Knowledge Graphs
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Day 230: NLP Papers Summary – Building and Exploring an EKG for Investment Analysis – Approach Overview
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Day 229: NLP Papers Summary – Building and Exploring an EKG for Investment Analysis – Introduction and Challenges
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Day 228: Learn NLP With Me – Knowledge Graph on Finance (Balance Sheets)
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Day 227: Learn NLP With Me – Translate model for Knowledge Graph Embedding
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Day 226: NLP Papers Summary – Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach I
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Day 225: NLP Papers Summary – Architecture of Knowledge Graph Construction Techniques
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Day 224: Learn NLP With Me – SLP Textbook Ch.22 – Coreference Resolution VI
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Day 223: Learn NLP With Me – SLP Textbook Ch.22 – Coreference Resolution V
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Day 222: Learn NLP With Me – SLP Textbook Ch.22 – Coreference Resolution IV
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Day 221: Learn NLP With Me – SLP Textbook Ch.22 – Coreference Resolution III
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Day 220: Learn NLP With Me – SLP Textbook Ch.22 – Coreference Resolution II
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Day 219: Learn NLP With Me – SLP Textbook Ch.22 – Coreference Resolution I
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Day 218: Learn NLP With Me – SLP Textbook Ch.7 – Neural Networks and Neural Language Models II
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Day 217: Learn NLP With Me – SLP Textbook Ch.7 – Neural Networks and Neural Language Models I
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Day 216: Learn NLP With Me – SLP Textbook Ch.21 – Lexicons for Sentiment, Affect, and Connotation IV
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Day 215: Learn NLP With Me – SLP Textbook Ch.21 – Lexicons for Sentiment, Affect, and Connotation III
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Day 214: Learn NLP With Me – SLP Textbook Ch.21 – Lexicons for Sentiment, Affect, and Connotation II
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Day 213: Learn NLP With Me – SLP Textbook Ch.21 – Lexicons for Sentiment, Affect, and Connotation I
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Day 212: K-Means Clustering using SK-Learn and NLTK (Quick Read)
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Day 211: When to use which clustering algorithms?
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Day 210: Describing 4 different clustering algorithms
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Day 209: Introduction to Clustering
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Day 208: Learning PyTorch – Fine Tuning BERT for Sentiment Analysis (Part Two)
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Day 207: Learning PyTorch – Fine Tuning BERT for Sentiment Analysis (Part One)
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Day 206: NLP Papers Summary – Transformers and Pointer-Generator Networks for Abstractive Summarization
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Day 205: Learn NLP With Me – Zero-Shot Learning for Text Classification
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Day 204: Learn NLP With Me – Subword Tokenisation and Normalisation
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Day 203: Learn NLP With Me – Attention Mechanism and Transformers Revisit
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Day 202: Learn NLP With Me – NLP and Transfer Learning Revisit
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Day 201: Abbreviation Resolution and UMLS Entity Linking using SciSpaCy
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Day 200: Learn NLP With Me – Filling the Gaps with NLP Interview Questions IV
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Day 199: Learn NLP With Me – Filling the Gaps with NLP Interview Questions III
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Day 198: Learn NLP With Me – Filling the Gaps with NLP Interview Questions II
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Day 197: Learn NLP With Me – Filling the Gaps with NLP Interview Questions
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Day 196: Coreference Resolution with NeuralCoref (SpaCy)
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Day 195: Learn NLP With Me – What is Coreference Resolution?
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Day 194: Learning PyTorch – Tweets Sentiment Extraction (Part 2)
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Day 193: Learning PyTorch – Tweets Sentiment Extraction (Part 1)
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Day 192: NLP Papers Summary – Guiding Extractive Summarization with Question-Answering Rewards
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Day 191: Summarisation of arXiv papers using TextRank – Does it work?
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Day 190: Learning PyTorch – PyTorch Lightning Structure (with codes)
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Day 189: Learning PyTorch – PyTorch Lightning Introduction
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Day 188: NLP Papers Summary – A Supervised Approach to Extractive Summarisation of Scientific Papers
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Day 187: Learn NLP With Me – Embeddings of Language, Knowledge Representation, and Reasoning
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Day 186: NLP Papers Summary – Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge
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Day 185: NLP Papers Summary – A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
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Day 184: Learning PyTorch – Machine Translation with TorchText
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Day 183: Learning PyTorch – TorchText Introduction
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Day 182: Learning PyTorch – Custom Dataset and DataLoader
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Day 181: Learning PyTorch – Language Model with nn.Transformer and TorchText (Part 2)
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Day 180: Learning PyTorch – Language Model with nn.Transformer and TorchText (Part 1)
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Day 179: Learning PyTorch – Revisiting Concepts
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Day 178: NLP Papers Summary – GPT-3 : Broader Impacts
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Day 177: NLP Papers Summary – GPT-3 : Limitations
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Day 176: NLP Papers Summary – GPT-3 : Training and Evaluation Methods
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Day 175: NLP Papers Summary – GPT-3 : Introduction and Context
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Day 174: NLP Papers Summary – PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
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Day 173: NLP Discovery – Text-To-Text Transfer Transformer (T5)
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Day 172: Learn NLP With Me – Fast.Ai NLP Course – Disinformation in Text (END COURSE)
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Day 171: Learn NLP With Me – Fast.Ai NLP Course – Transformers and Language Translation
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Day 170: Learn NLP With Me – Fast.Ai NLP Course – Algorithmic Bias
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Day 169: Learn NLP With Me – Fast.Ai NLP Course – Word Embeddings Quantify Stereotypes and Text Generation Algorithms
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Day 168: Learn NLP With Me – Fast.Ai NLP Course – Understanding RNNs and Seq2Seq Translation
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Day 167: NLP Papers Summary – Ontology-Aware Clinical Abstractive Summarization
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Day 166: NLP Papers Summary – Publicly Available Clinical BERT Embeddings
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Day 165: Learn NLP With Me – Fast.Ai NLP Course – ULMFit for non-English Languages
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Day 164: Learn NLP With Me – Fast.Ai NLP Course – Transfer Learning
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Day 163: How to build a Language Model from scratch – Implementation
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Day 162: Learn NLP With Me – Fast.Ai NLP Course – Revisiting Naïve Bayes & Regex
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Day 161: NLP Papers Summary – BLEURT: Learning Robust Metrics for Text Generation
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Day 160: NLP Papers Summary – Extractive Summarization as Text Matching
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Day 159: NLP Papers Summary – ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network
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Day 158: NLP Papers Summary – Embarrassingly Simple Unsupervised Aspect Extraction
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Day 157: NLP Papers Summary – Explainable Prediction of Medical Codes from Clinical Text
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Day 156: NLP Papers Summary – Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
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Day 155: NLP Papers Summary – TRAIN ONCE, TEST ANYWHERE: ZERO-SHOT LEARNING FOR TEXT CLASSIFICATION
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Day 154: NLP Papers Summary – Contextual Embeddings: When Are They Worth It?
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Day 153: NLP Papers Summary – Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
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Day 152: NLP Papers Summary – OPINIONDIGEST: A Simple Framework for Opinion Summarization
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Day 151: NLP Papers Summary – A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
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Day 150: NLP Papers Summary – Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter
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Day 149: NLP Papers Summary – MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
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Day 148: NLP Papers Summary – A Transformer-based Approach for Source Code Summarization
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Day 147: NLP Papers Summary – Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data
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Day 146: NLP Papers Summary – Exploring Content Selection in Summarization of Novel Chapters
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Day 145: NLP Papers Summary – SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
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Day 144: NLP Papers Summary – Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
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Day 143: NLP Papers Summary – Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
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Day 142: NLP Papers Summary – Measuring Emotions in the COVID-19 Real World Worry Dataset
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Day 141: NLP Papers Summary – TextAttack: A Framework for Adversarial Attacks in Natural Language Processing
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Day 140: NLP Papers Summary – Multimodal Machine Learning for Automated ICD Coding
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Day 139: NLP Papers Summary – Neural Approaches to Conversational AI – Conclusion & Research Trends
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Day 138: NLP Papers Summary – Neural Approaches to Conversational AI – Conversational AI in Industry
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Day 137: NLP Papers Summary – Neural Approaches to Conversational AI – Social Bot’s Landscape
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Day 136: NLP Papers Summary – Neural Approaches to Conversational AI – Social Bot’s Challenges
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Day 135: NLP Papers Summary – Neural Approaches to Conversational AI – E2E Social Bots
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Day 134: NLP Papers Summary – Neural Approaches to Conversational AI – NLG and E2E
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Day 133: NLP Papers Summary – Neural Approaches to Conversational AI – NLU and DST
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Day 132: NLP Papers Summary – Neural Approaches to Conversational AI – Task-Oriented Systems (Evaluation Metrics)
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Day 131: NLP Papers Summary – Neural Approaches to Conversational AI – Task-Oriented Systems (Introduction)
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Day 130: NLP Papers Summary – Neural Approaches to Conversational AI – Text-QA (MRC)
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Day 129: NLP Papers Summary – Neural Approaches to Conversational AI – KB-QA (Neural Methods)
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Day 128: NLP Papers Summary – Neural Approaches to Conversational AI – KB-QA (Symbolic Methods)
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Day 127: NLP Papers Summary – Neural Approaches to Conversational AI – Introduction
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Day 126: NLP Papers Summary – Neural News Recommendation with Topic-Aware News Representation
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Day 125: NLP Papers Summary – A2N: Attending to Neighbors for Knowledge Graph Inference
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Day 124: NLP Papers Summary – TLDR: Extreme Summarization of Scientific Documents
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Day 123: NLP Papers Summary – Context-aware Embedding for Targeted Aspect-based Sentiment Analysis
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Day 122: NLP Papers Summary – Applying BERT to Document Retrieval with Birch
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Day 121: NLP Papers Summary – Concept Pointer Network for Abstractive Summarization
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Day 120: NLP Papers Summary – A Simple Theoretical Model of Importance for Summarization
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Day 119: NLP Papers Summary – An Argument-Annotated Corpus of Scientific Publications
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Day 118: NLP Papers Summary – Extractive Summarization of Long Documents by Combining Global and Local Context
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Day 117: NLP Papers Summary – Abstract Text Summarization: A Low Resource Challenge
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Day 116: NLP Papers Summary – Data-driven Summarization of Scientific Articles
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Day 115: NLP Papers Summary – SCIBERT: A Pretrained Language Model for Scientific Text
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Day 114: NLP Papers Summary – A Summarization System for Scientific Documents
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Day 113: NLP Papers Summary – On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
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Day 112: NLP Papers Summary – A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis
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Day 111: NLP Papers Summary – The Risk of Racial Bias in Hate Speech Detection
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Day 110: NLP Papers Summary – Double Embeddings and CNN-based Sequence Labelling for Aspect Extraction
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Day 109: NLP Papers Summary – Studying Summarization Evaluation Metrics in the Appropriate Scoring Range
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Day 108: NLP Papers Summary – Simple BERT Models for Relation Extraction and Semantic Role Labelling
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Day 107: NLP Papers Summary – Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
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Day 106: NLP Papers Summary – An Unsupervised Neural Attention Model for Aspect Extraction
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Day 105: NLP Papers Summary – Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks
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Day 104: NLP Papers Summary – SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
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Day 103: NLP Papers Summary – Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
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Day 102: NLP Papers Summary – Implicit and Explicit Aspect Extraction in Financial Microblogs
Data Science
Day 101: In-depth study of RASA’s DIET Architecture
Data Science
Day 100: Learn PGM with Me – Representation – Introduction to Conditional Random Fields
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Day 99: Learn PGM with Me – Representation – Markov Random Fields vs Bayesian Networks
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Day 98: Learn PGM with Me – Representation – Introduction to Markov Random Fields
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Day 97: Learn PGM with Me – Representation – Dependencies of a Bayes’ Network
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Day 96: Learn PGM with Me – Representation – Introduction to Bayesian Networks
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Day 95: Learn PGM with Me – Probability Review for Graphical Models – Two Random Variables
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Day 94: Learn PGM with Me – Probability Review for Graphical Models – Random Variables
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Day 93: Learn PGM with Me – Probability Review for Graphical Models – Elements of probability
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Day 92: Learn PGM with Me – Probability Review for Graphical Models
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Day 91: Learn PGM with Me – The 3 Main Aspects of Graphical Models
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Day 90: Learn PGM with Me – What is Probabilistic Graphical Modelling?
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Day 89: Deep Generative Models and NLP
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Day 88: What is Autoencoders?
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Day 87: Learn NLP with Me – BERT on Sentiment Analysis
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Day 86: Mini NLP Data Science Project – Implementation VII – Text Similarity
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Day 85: Mini NLP Data Science Project – Implementation VI – Topic Modelling Analysis
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Day 84: Mini NLP Data Science Project – Implementation V – Text Clustering III
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Day 83: Mini NLP Data Science Project – Implementation IV – Text Clustering II
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Day 82: Mini NLP Data Science Project – Implementation III – Text Clustering I
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Day 81: Mini NLP Data Science Project – Implementation II – Text Processing
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Day 80: Mini NLP Data Science Project – Implementation I – EDA
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Day 79: Mini NLP Data Science Project – Implementation Series – Introduction
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Day 78: Learn NLP with Me – FLT – Context-free Languages – Chomsky Normal Form
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Day 77: Learn NLP with Me – FLT – Context-free Languages – Context-free Grammars
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Day 76: Learn NLP with Me – Formal Language Theory – Context-free Languages – Introduction
Data Science
Day 75: Learn NLP with Me – I.E. – Hedges, Denials, and Hypotheticals – Handling Modality
Data Science
Day 74: Learn NLP with Me – I.E. – Hedges, Denials, and Hypotheticals – Introduction
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Day 73: Learn NLP with Me – Information Extraction – Events
Data Science
Day 72: Learn NLP with Me – I.E. – Relations – Open Information Extraction
Data Science
Day 71: Learn NLP with Me – I.E. – Knowledge Base Population – Distant Supervision
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Day 70: Learn NLP with Me – I.E. – Knowledge Base Population – Information Fusion
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Day 69: Learn NLP with Me – Information Extraction – Relations – Knowledge Base Population
Data Science
Day 68: Learn NLP with Me – Information Extraction – R.E. as Classification Task – Neural Method
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Day 67: Learn NLP with Me – fast.ai NLP course – Derivation of Naïve Bayes & Numerical Stability
Data Science
Day 66: Learn NLP with Me – fast.ai NLP course – Sentiment Classification with Naïve Bayes & Logistic Regression
Data Science
Day 65: Learn NLP with Me – Information Extraction – R.E. as Classification Task – Kernel Method
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Day 64: Learn NLP with Me – Information Extraction – Relations – Relation Extraction as Classification Task
Data Science
Day 63: Learn NLP with Me – FLT – Regular Languages – Finite state composition
Data Science
Day 62: Learn NLP with Me – FLT – Regular Languages – Inflectional Morphology
Data Science
Day 61: What is Semantic Textual Similarity?
Data Science
Day 60: Learn NLP with Me – FLT – Regular Languages – Finite State Transducers
Data Science
Day 59: Learn NLP with Me – Formal Language Theory – Regular Languages – Weighted FSAs
Data Science
Day 58: Learn NLP with Me – Formal Language Theory – Regular Languages – Morphology Analysis
Data Science
Day 57: Learn NLP with Me – fast.ai NLP course – Sentiment Classification with Naïve Bayes
Data Science
Day 56: Learn NLP with Me – Formal Language Theory – Regular Languages – Finite State Acceptors
Data Science
Day 55: Learn NLP with Me – Formal Language Theory – Regular Languages – Introduction
Data Science
Day 54: Learn NLP with Me – Formal Language Theory – Introduction
Data Science
Day 53: Learn NLP with Me – Information Extraction – Relations – Pattern-based Relation Extraction
Data Science
Day 52: Learn NLP with Me – Information Extraction – Relations – Introduction
Data Science
Day 51: Learn NLP with Me – Information Extraction – Entities – Collective Entity Linking
Data Science
Day 50: NLP Discovery – Turing-NLG – A 17-billion parameter Language Model
Data Science
Day 49: Learning PyTorch – Training an Image Classifier
Data Science
Day 48: Learning PyTorch – Training a Neural Network
Data Science
Day 47: Learning PyTorch – Autograd – Automatic Differentiation
Data Science
Day 46: Learning PyTorch – A Deep Learning Framework – Introduction to Tensors
Data Science
Day 45: Learn NLP with Me – Information Extraction – Entities – Entity linking by learning to rank
Data Science
Day 44: Learn NLP with Me – Information Extraction – Entities
Data Science
Day 43: Learn NLP with Me – Information Extraction – Introduction
Data Science
Day 42: Learn NLP with Me – fast.ai NLP course – Topic Modelling & SVD revisited
Data Science
Day 41: Learn NLP with Me – fast.ai NLP course – Topic Modelling with SVD & NMF
Data Science
Day 40: What is Neural Architecture Search (NAS)?
Data Science
Day 39: What is Perplexity?
Data Science
Day 38: NLP Discovery – Google’s Chatbot Meena
Data Science
Day 37: Learn NLP with Me – fast.ai NLP course – What is NLP?
Data Science
Day 36: Learn NLP with Me – MRC – New Trends III
Data Science
Day 35: Learn NLP with Me – MRC – New Trends II
Data Science
Day 34: Learn NLP with Me – MRC – New Trends I
Data Science
Day 33: Learn NLP with Me – MRC – Open Issues
Data Science
Day 32: Learn NLP with Me – MRC – Deep Learning VI – Additional Tricks
Data Science
Day 31: Learn NLP with Me – MRC – Deep Learning V – Answer Prediction
Data Science
Day 30: Learn NLP with Me – MRC – Deep Learning IV – Context Question Interaction
Data Science
Day 29: Learn NLP with Me – MRC – Deep Learning III – Feature Extraction
Data Science
Day 28: Learn NLP with Me – MRC – Deep Learning II – Embeddings
Data Science
Day 27: Learn NLP with Me – MRC – Deep Learning I – General Architecture
Data Science
Day 26: Learn NLP with Me – Machine Reading Comprehension – Datasets & Evaluation Metrics
Data Science
Day 25: Learn NLP with Me – Machine Reading Comprehension – MRC Tasks
Data Science
Day 24: Learn NLP with Me – Machine Reading Comprehension – Introduction
Data Science
Day 23: Summarisation – ROUGE score
Data Science
Day 22: TFIDF for Summarisation – Implementation VI – Putting It All Together
Data Science
Day 21: TFIDF for Summarisation – Implementation V – Summary Generation
Data Science
Day 20: TFIDF for Summarisation – Implementation IV – TFIDF Matrix & Sentence Scoring
Data Science
Day 19: TFIDF for Summarisation – Implementation III – IDF Matrix
Data Science
Day 18: TFIDF for Summarisation – Implementation II – Term Frequency (TF) Matrix
Data Science
Day 17: TFIDF for Summarisation – Implementation I – Constructing a Class
Data Science
Day 16: TextRank – Manual Implementation (Code)
Data Science
Day 15: TextRank for Summarisation (Code – Gensim)
Data Science
Day 14: Convolutional Neural Network in NLP
Data Science
Day 13: Seq2Seq
Data Science
Day 12: Recurrent Neural Network
Data Science
Day 11 – Transformers: Positioning Encoding and Decoder
Data Science
Day 10: Transformers – MultiHead Attention Mechanism
Data Science
Day 9: Transformers – Introduction
Data Science
Day 8: TextRank for Summarisation
Data Science
Day 7: Term Frequency-Inverse Document Frequency (TFIDF) for Summarisation
Natural Language Processing
Day 6: projectJarvis – Retrieving medium articles (Code)
Natural Language Processing
Day 5: projectJarvis – Retrieving medium articles
Natural Language Processing
Day 4: projectJarvis – Introduction (#project2020)
Natural Language Processing
Day 3: Word Embeddings
Natural Language Processing
Day 2: Damerau-Levenshtein Distance
what is NLP
Natural Language Processing
Day 1: What is Natural Language Processing