topic-indexing
NOTE:
This method is an outdated hack for generating keyword sets. If you're looking to do semi-supervised topic classification, there are more recent methods you should consider, e.g.:
- Keyword-Assisted Topic Models: https://imai.fas.harvard.edu/research/files/keyATM.pdf
- Seeded-LDA: https://towardsdatascience.com/why-to-use-seeded-topic-models-in-your-next-project-and-how-to-implement-them-in-r-8502d15d6e8d
The tools in this repo use word embeddings to create topic-specific index of a provided corpus of text documents or conversations. (By "index" here we mean the kind you'd find in the back of a book.)
So far, all that's available is a tool (generate_topic_terms.py) that generates a configurably
sized set of words and phrases related to a given topic, after you describe the topic with a short
set of seed terms. Please see the configuration file topics.yml for more information on how this works.
This tool does not use the corpus that you intend to index, but it references a word embedding model that may be trained partly or entirely on the corpus.
There are two word2vec models in the models directory of this repository. Both are trained on
ASR-transcribed talk radio programs that emphasize discussion of the news in United States communities.
See the RadioTalk project (see https://github.com/social-machines/RadioTalk) for information
on how the system was built.
radiotalk_2019 is trained on 3 months of transcripts (from October through December of 2018)
radiotalk_2020-04 is trained on a more recent sample of similar
data (mid-February through mid-April of 2020), mixed with the data from radiotalk_2019.
Run this program without any arguments to generate a JSON file containing the topic terms we use for indexing Local Voices Network conversations.