Machine Reading Tea Leaves

[amritbhanu]

Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality

[bibtex](@inproceedings{lau2014machine,
title={Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality.},
author={Lau, Jey Han and Newman, David and Baldwin, Timothy},
booktitle={EACL},
pages={530--539},
year={2014}
})

General:

• A good paper which gives rational about the topics instability

Measures:

• notion of topic “coherence”, and proposed an automatic method for estimating topic coherence based on pairwise pointwise mutual information (PMI) between the topic words
• direct appraoch, asking people about topics, indirect approach by evaluating PMI, CP.
• To create gold-standard coherence judgements, they used Amazon Mechanical Turk

Problems:

• perplexity correlates negatively with topic interpretability

Research Question:

• word intrusion measures topic interpretability differently to observed coherence

Terminologies:

• topic coherence, the semantic interpretability of the top terms usually used to describe discovered topics
• “intruder word”, which has low probability in the topic of interest, but high probability in other topics