Generative linguistics | Grammar frameworks
Minimal recursion semantics (MRS) is a framework for computational semantics. It can be implemented in typed feature structure formalisms such as head-driven phrase structure grammar and lexical functional grammar. It is suitable for computational language parsing and natural language generation. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. This technique is used in machine translation. Early pioneers of MRS include Ann Copestake, , Carl Pollard, and Ivan Sag. (Wikipedia).
SYN_018 - Linguistic Micro-Lectures: Recursion
In this short micro-lecture, Victoria Galarneau, one of Prof. Handke's students, discusses the term 'recursion', a central notion in syntax.
From playlist Micro-Lectures - Syntax
From playlist Week 4 2015 Shorts
A Beginner's Guide to Recursion
Recursion has an intimidating reputation for being the advanced skill of coding sorcerers. But in this tutorial we look behind the curtain of this formidable technique to discover the simple ideas under it. Through live coding demos in the interactive shell, we'll answer the following que
From playlist Software Development
Recursive Functions (Discrete Math)
This video introduces recursive formulas.
From playlist Functions (Discrete Math)
Sequences: Introduction to Solving Recurrence Relations
This video introduces solving recurrence relations by the methods of inspection, telescoping, and characteristic root technique. mathispower4u.com
From playlist Sequences (Discrete Math)
Applying the recursive formula to a sequence to determine the first five terms
👉 Learn all about recursive sequences. Recursive form is a way of expressing sequences apart from the explicit form. In the recursive form of defining sequences, each term of a sequence is expressed in terms of the preceding term unlike in the explicit form where each term is expressed in
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Building Your Own Dynamic Language
February 14, 2007 lecture by Ian Piumarta for the Stanford University Computer Systems Colloquium (EE 380). Ian describes several significant aspects of the design and implementation of a programming environment that, along with a programming language, exhibits the properties desired of th
From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)
Lecture 14: Tree Recursive Neural Networks and Constituency Parsing
Lecture 14 looks at compositionality and recursion followed by structure prediction with simple Tree RNN: Parsing. Research highlight ""Deep Reinforcement Learning for Dialogue Generation"" is covered is backpropagation through Structure. Key phrases: RNN, Recursive Neural Networks, MV-RN
From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)
In this tool-assisted education video I create a parser in C++ for a B-like programming language using GNU Bison. For the lexicographical analysis, a lexer is generated using re2c. This is part of a multi-episode series. In the next video, we will focus on optimization. Become a member:
From playlist Creating a Compiler
Python - Analyzing Sentence Structure
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This video covers how to analyze sentence structure using Python and nltk. This video scales up the parsing discussion from the previous chapter, looking more at parse trees and how to parse effectively. Y
From playlist Natural Language Processing
R & Python - Parsing Part 1 (2022)
Lecturer: Dr. Erin M. Buchanan Spring 2022 https://www.patreon.com/statisticsofdoom This video is part of my Natural Language Processing course. This video covers parsing, which is creating sentence structure for understanding meaning. You will learn both traditional constituency parsing
From playlist Natural Language Processing
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3wL2FCD Professor Christopher Manning, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Lear
From playlist Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019
How to use the recursive formula to evaluate the first five terms
👉 Learn all about recursive sequences. Recursive form is a way of expressing sequences apart from the explicit form. In the recursive form of defining sequences, each term of a sequence is expressed in terms of the preceding term unlike in the explicit form where each term is expressed in
From playlist Sequences
Semantics of Higher Inductive Types - Michael Shulman
Semantics of Higher Inductive Types Michael Shulman University of California, San Diego; Member, School of Mathematics February 27, 2013
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Deep Learning 6: Deep Learning for NLP
From playlist Learning resources
MountainWest RubyConf 2015 - Standing on the Shoulders of Giants
by Ryan Davis Ruby is a fantastic language, but it could be better. While it has done a terrific job of taking ideas from languages like smalltalk, lisp, and (to an extent) perl, it hasn’t done nearly enough of it. Big thinkers like Alan Kay & Dan Ingalls (smalltalk), David Ungar (self), G
From playlist MWRC 2015
Applying the recursive formula to a geometric sequence
👉 Learn all about recursive sequences. Recursive form is a way of expressing sequences apart from the explicit form. In the recursive form of defining sequences, each term of a sequence is expressed in terms of the preceding term unlike in the explicit form where each term is expressed in
From playlist Sequences
DeepMind x UCL | Deep Learning Lectures | 9/12 | Generative Adversarial Networks
Generative adversarial networks (GANs), first proposed by Ian Goodfellow et al. in 2014, have emerged as one of the most promising approaches to generative modeling, particularly for image synthesis. In their most basic form, they consist of two "competing" networks: a generator which trie
From playlist Learning resources