BibSonomy bookmarks for /tag/topic

Research Gap & Topic Development from Evidence, Not Guesswork

researchedit4u — 2026-09-30T13:54:09+02:00

Choosing a broad or fashionable topic does not automatically create a defensible research gap. Strong research topics emerge from unresolved questions, conflicting findings, methodological limitations or meaningful problems that existing studies have not adequately addressed. ResearchEdit4U supports research gap and topic development through structured literature comparison, problem refinement and feasibility assessment. Before finalising your topic, map what is already known, what remains unresolved, why that missing knowledge matters and whether your study can realistically address it.

1 on 1 Mentoring Research Services - DBA proposal & Dissertation Help

rmcdr — 2024-07-30T11:15:58+02:00

Get hands-on help from your dedicated Research Coach. Our 1:1 Mentoring Services offer personalised guidance to excel in every stage of your research journey.

tomotopy

bsc — 2021-11-10T15:01:02+01:00

tomotopy is a Python extension of tomoto (Topic Modeling Tool) which is a Gibbs-sampling based topic model library written in C++. It utilizes a vectorization of modern CPUs for maximizing speed. The current version of tomoto supports several major topic models including

Kafka Performance and Resource Considerations

becker — 2018-10-08T08:48:11+02:00

https://www.cloudera.com/documentation/kafka/1-2-x/topics/kafka_performance.html

lda2vec – flexible & interpretable NLP models — lda2vec 0.01 documentation

schwemmlein — 2018-06-28T12:43:51+02:00

http://lda2vec.readthedocs.io/en/latest/?badge=latest

How to easily do Topic Modeling with LSA, PSLA, LDA & lda2Vec

schwemmlein — 2018-06-28T12:41:31+02:00

In natural language understanding, there is a hierarchy of lenses through which we can extract meaning - from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics.

Things That Cannot Be Communicated | Scott Adams Blog

bshanks — 2016-12-12T10:00:08+01:00

http://blog.dilbert.com/post/117606386746/things-that-cannot-be-communicated

GitHub - blei-lab/class-slda: Implements supervised topic models with a categorical response.

ans — 2016-05-17T11:55:57+02:00

https://github.com/blei-lab/class-slda

Labels and Patterns - David Mimno

schwemmlein — 2016-04-21T19:21:19+02:00

http://www.mimno.org/articles/labelsandpatterns/

tmw-tutorial · Penflip

schwemmlein — 2016-03-17T14:51:12+01:00

Topic modeling tutorial (Christof Schöch)

Mapping literacy spaces in motion: A rhizomatic analysis of a classroom literacy performance - LEANDER - 2011 - Reading Research Quarterly - Wiley Online Library

umatadema — 2016-02-04T12:06:09+01:00

In many literacy classrooms, students engage in public performances in which they use various texts, movements of their bodies, and verbal interactions. How do we interpret such events? In this article, we critique a representational mode of interpretation and describe an alternate mode. We argue that literacy performances are often about creating differences, including differences in the moving, shifting relations of semiotic resources and differences in the performed identities of participants. Such differences—effects and affective intensities—are lost or overly stabilized within conventional interpretations, which focus on asking how meanings are represented, organized, and produced in performances. Conventionally, the texts of performances (e.g., print, images, speech) are imagined to signify (or re-present) a world that lies behind them. The task of interpretation is approached as reading for meaning. In this mode, we conceive of performances as primarily communicational or informational. Alternatively, using rhizomatic analysis, we follow the emergence of relations and differences by mapping performance-in-motion. We discuss how rhizomatic analysis shifts attention away from fixed meanings and toward action and the new “becomings” that are an important part of literacy performances. Data are drawn from an ethnographic study of interactions in a socially, culturally, and racially diverse high school American Studies classroom.

wbuntine/topic-models · GitHub

schwemmlein — 2016-01-12T16:14:20+01:00

topic-models - Topic modelling software using non-parametric methods

UW Interactive Data Lab | Papers

schwemmlein — 2016-01-11T13:53:47+01:00

Termite: Visualization Techniques for Assessing Textual Topic Models

After “Topic detection or topic construction?” - a workshop report - KNOWeSCAPE | KNOWeSCAPE

stumme — 2015-11-05T13:37:02+01:00

http://knowescape.org/after-topic-detection-or-topic-construction-a-workshop-report/

Topical Issues | Topical insights into components of prior research and aspects of latent academia

schwemmlein — 2015-08-19T09:43:19+02:00

Topic models blog by Wray Buntine

Stan: Project Home Page

schwemmlein — 2015-05-27T12:37:00+02:00

Stan modeling language and C++ library for Bayesian inference. NUTS adaptive HMC (MCMC) sampling, automatic differentiation, R, shell interfaces. Gelman.

Topic modeling with latent Dirichlet allocation in Python

schwemmlein — 2015-05-11T14:39:13+02:00

Topic modeling with latent Dirichlet allocation in Python

lda 1.0.2 : Python Package Index

schwemmlein — 2015-05-11T14:38:10+02:00

Topic modeling with latent Dirichlet allocation

Main Page

becker — 2015-05-02T22:57:57+02:00

http://www.idiap.ch/resource/research/probamod-v1/index.html

Home Page for 20 Newsgroups Data Set

schwemmlein — 2015-04-28T20:23:43+02:00

http://qwone.com/~jason/20Newsgroups/