Data-oriented parsing (DOP, also data-oriented processing) is a probabilistic model in computational linguistics. DOP was conceived by Remko Scha in 1990 with the aim of developing a performance-oriented grammar framework. Unlike other probabilistic models, DOP takes into account all subtrees contained in a treebank rather than being restricted to, for example, 2-level subtrees (like PCFGs), thus allowing for more context-sensitive information. Several variants of DOP have been developed. The initial version developed by Rens Bod in 1992 was based on , while more recently, DOP has been combined with lexical-functional grammar (LFG). The resulting DOP-LFG finds an application in machine translation. Other work on learning and parameter estimation for DOP has also found its way into machine translation. (Wikipedia).
Intro to Data Science: Historical Context
This lecture provides some historical context for data science and data-intensive scientific inquiry. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
Learn Data Science Tutorial - Full Course for Beginners
Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the "sexiest job of the 21st century." You'll learn the important elements of data science. You'll be introduced to the principles, practices, and tools that make data science the powerful me
From playlist Data Science
A #database #index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure. Indexes are used to quickly locate data without having to search every row in a database
From playlist Database
Data science describes the activities related to collecting, storing and creating value from data. Creating value from data means using it to do useful things, like making better decisions. By analyzing data we can detect patterns in it and understand the process that generated it. This i
From playlist Data Science Dictionary
R programming for Beginners | R programming for data Science
R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. This video is a
From playlist Programming
Data Modeling Tutorial | Data Modeling for Data Warehousing | Data Warehousing Tutorial | Edureka
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, th
From playlist Data Warehousing Tutorial Videos
Data Science Tutorial for Beginners - 1 | What is Data Science? | Data Analytics Tools | Edureka
( Data Science Training - https://www.edureka.co/data-science ) Data Science Blog Series: https://goo.gl/1CKTyN http://www.edureka.co/data-science Please write back to us at sales@edureka.co or call us at +91-8880862004 for more information. Data Science is all about extracting knowledge
From playlist Data Science Training Videos
Network Operations | Android App Development Tutorial For Beginners
🔥Post Graduate Program In Full Stack Web Development: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=NetworkOperations-zsOLq4iuXhU&utm_medium=Descriptionff&utm_source=youtube 🔥Caltech Coding Bootcamp (US Only): https://www.simplilearn.
From playlist Android App Development Tutorial Videos [Updated]
Business Data Analysis with Excel
Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation wher
From playlist Data Analytics Tutorials
Dynamic Javascript Scraping - Web scraping with Beautiful Soup 4 p.4
Welcome to part 4 of the web scraping with Beautiful Soup 4 tutorial mini-series. Here, we're going to discuss how to parse dynamically updated data via javascript. Many websites will supply data that is dynamically loaded via javascript. In Python, you can make use of jinja templating an
From playlist Web scraping and parsing with Beautiful Soup & Python
Rasa Reading Group: Task-Oriented Dialogue as Dataflow Synthesis
This week the reading group kicks off a new paper, "Task-Oriented Dialogue as Dataflow Synthesis" (Transactions of the Association for Computational Linguistics, 8, 556-571, 2020) by Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLo
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ShmooCon 2013: From "Shotgun Parsers" to Better Software Stacks
For more information and to download the video visit: http://bit.ly/shmoocon2013 Playlist ShmooCon 2013: http://bit.ly/Shmoo13 Speakers: Meredith Patterson, Sergey Bratus and Dan 'TQ' Hirsh Everyone agrees that aggressive input checking and validation of input-handling code are crucial t
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Monadic Programming: With Application to Data Analysis, Machine Learning and Language Processing
To learn more about Wolfram Technology Conference, please visit: https://www.wolfram.com/events/technology-conference/ Speaker: Anton Antonov Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and
From playlist Wolfram Technology Conference 2017
Is C++20 a language that supports a functional style of programming? Can we write modern C++ code in a pure functional style that would easily translate into a pure functional language like Haskell, and could that C++ code end up looking just as nice while still being reasonably efficient?
From playlist C++
Top 10 Python Libraries For Data Science | #python #datascience #programming
Don’t forget to subscribe! Top 10 Python Libraries For Data Science. This Python tutorial is about the top 10 Python libraries for data science. Python is the most widely used programming language today. It is easy to learn & debug, widely used, object-oriented, open source, high-perform
From playlist Programming Tutorials
Intro to Data Science: Answering Questions with Data
This lecture describes the central aspect of data science: asking and answering questions with data. In particular, we discuss the thought process and progression of questions one might ask and answer with data science. Book website: http://databookuw.com/ Steve Brunton's website: eigen
From playlist Intro to Data Science
Black Hat USA 2010: Constricting the Web: Offensive Python for Web Hackers 1/4
Clip 1/4 Speakers: Nathan Hamiel, Marcin Wielgoszewski It seems that everything is a web application nowadays. Whether the application is cloud-based, mobile, or even fat client they all seem to be using web protocols to communicate. Adding to the traditional landscape there is rise in
From playlist Black Hat USA 2010
The LAPPS Grid and Galaxy, 20160628
Galaxy Community Conference 2016, Indiana University - Bloomington | https://gcc2016.iu.edu/ https://gcc16.sched.com/event/793ebb7ebe8fe061e12bf270b147e146# Authors: Nancy Ide, Keith Suderman, James Pustejovsky, Marc Verhagen, Eric Nyberg, Chris Cieri Abstract The NSF/SI2-funded Langua
From playlist 2016 Galaxy User Community Conference (GCC16)
CMU Neural Nets for NLP 2017 (14): Neural Semantic Parsing
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * What is Graph-based Parsing? * Minimum Spanning Tree Parsing * Structured Training and Other Improvements * Dynamic Programming Methods for Phrase Structure Parsing * Reranking Slides: http:/
From playlist CMU Neural Nets for NLP 2017
Data Visualization: Types of Data
Here I introduce different types of data and highlight common ways to visualize them. Bing Brunton's website: www.bingbrunton.com
From playlist Intro to Data Science