Word order | Generative syntax
Topicalization is a mechanism of syntax that establishes an expression as the sentence or clause topic by having it appear at the front of the sentence or clause (as opposed to in a canonical position further to the right). This involves a phrasal movement of determiners, prepositions, and verbs to sentence-initial position. Topicalization often results in a discontinuity and is thus one of a number of established discontinuity types, the other three being wh-fronting, scrambling, and extraposition. Topicalization is also used as a constituency test; an expression that can be topicalized is deemed a constituent. The topicalization of arguments in English is rare, whereas circumstantial adjuncts are often topicalized. Most languages allow topicalization, and in some languages, topicalization occurs much more frequently and/or in a much less marked manner than in English. Topicalization in English has also received attention in the pragmatics literature. (Wikipedia).
Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines. - Natural Language Processing (Part 1): Introduction to NLP & Data Science - Natural Language Processing (Part 2): Data Cleaning & Text Pre-Proces
From playlist Data Science Algorithms
Computational Linguistics I: Topic Modeling
From playlist Digging into Data
Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence [Paper Read Out Loud]
http://umiacs.umd.edu/~jbg//docs/2021_neurips_incoherence.pdf
From playlist Papers Read Aloud
Full paper available: https://www.cs.colorado.edu/~jbg/docs/nips2009-rtl.pdf
From playlist Research Talks
In this video, Professor Chris Bail gives an introduction to topic models- a method for identifying latent themes in unstructured text data. Link to slides: https://compsocialscience.github.io/summer-institute/2020/materials/day3-text-analysis/topic-modeling/Rpres/Topic_Modeling.html#/ Lin
From playlist All Videos
An Introduction to Topic Modeling
In this video, Professor Chris Bail gives an introduction to topic models- a method for identifying latent themes in unstructured text data. Link to slides: https://compsocialscience.github.io/s... Link to the annotated code from this video: https://compsocialscience.github.io/s... Links t
From playlist SICSS 2020
Topic Models: Gibbs Sampling (13c)
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://sites.google.com/umd.edu/2021cl1webpage/ (Including homeworks and reading.)
From playlist Advanced Data Science
Leveraging NLP to Extract Insights from Customer Conversations
Presented by: Dr. Swati Sharma – NLP Lead at Cedrus Digital Customer service is the support a business provides to answer customer’s questions and concerns. This is a domain which enables businesses to differentiate themselves and establish happy and loyal customers. Traditionally, this
From playlist NLP Summit 2021
SICSS 2018 - Topic Models/Structural Topic Models (Day 3. June 20, 2018)
Chris Bail talks about topic models and structural topic models at the 2018 Summer Institute in Computational Social Science at Duke University. Slides and materials available here: https://compsocialscience.github.io/summer-institute/2018/teaching-learning-materials
From playlist All Videos