Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with topic-models
Search options answers only
not deleted
user 81949
A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents.
2
votes
Accepted
When is it ok to *not* use a held-out set for topic model evaluation?
"but is this always necessary/appropriate?"
No, this is not always necessary. Many papers, e.g. "Improving Topic Models with Latent Feature Word Representations", use topic coherence and/or document …
- 241
1
vote
Using LDA in non-realtime twitter data
You might want to look at the Java package jLDADMM.
jLDADMM provides implementations of the LDA topic model and the one-topic-per-document Dirichlet Multinomial Mixture (DMM) model for modeling topi …
- 241
1
vote
Data set for document topic discovery
Following your latest comment with regards to "each document is talking about a unique topic", I would suggest you to try the mixture-of-unigrams topic model (i.e. one topic per document). The model d …
- 241
7
votes
Accepted
topic similarity semantic PMI between two words wikipedia
You might compute PMI using Wikipedia, as following:
1) Using Lucene to index a Wikipedia dump
2) Using Lucene API, it is straightforward to get:
The number (N1) of documents containing word1 and …
- 241