Topic Coherence

Evaluating unsupervised topic models is tricky business. If the resulting model is not employed in retrieval, classification, or regression there really is no way of convincing someone of the model’s worth. You may, rightly, say that there is no use for an unsupervised model without one of these objectives and that the unsupervised soubriquet serves only to distinguish it from a model whose optimization procedure includes supervision in the form of labels or outputs. The only way a generative model will have a meaning of its own is if there is a natural or physical interpretation of the generative process itself picked out by its inference over the given samples.

Nevertheless, people try to evaluate unsupervised generative models without labels of some kind. A popular method is to hold-out a portion of the data for testing and to compute its log-likelihood \log p(\mathbf{w}) (also called predictive log-likelihood) by integrating out latent variables. While this works for any probabilistic model, a slightly different metric is employed called predictive-perlexity for textual models

\displaystyle  perplexity(\mathbf{w}^{\text{test}}) =   \exp \left( - \frac{\sum_{d=1}^M \log p(\mathbf{w}^{\text{text}}_d) }                    {\sum_{d=1}^M N_d}        \right)

where N_d is the number of words in document d.

Topic Coherence

The worth of this metric with respect to human interpretations of text is contested [2]. The authors Mimno et. al [2] suggest an alternative method of evaluation called topic coherence. The idea here is to prefer topics whose most frequent words appear, in general, together than apart. Meaning, a topic shouldn’t consist of many separate central words around which a host of supporting words are found; instead, a topic should consist of one coherent set of words. You can imagine this as being able to give a single solid name for each topic rather than having to choose from many ambiguous possibilities. The evaluation is expressed as follows

\displaystyle  C(t,V^{(t)}) = \sum_{m=2}^M \sum_{l=1}^{m-1} \log \frac{D(v^t_m, v^t_l)+1}{D(v^{t}_l)}

where V^t = (v^t_1,\dots, v^t_M) is a list of the M most probable words in topic t. Exponentiating this we find that is is computing the empirical joint probability of all the pairwise combinations of the top M words in a topic t.

Example

Recent work has focused on improving topic models in this regard with the addition of external knowledge about textual behavior such as knowing which words can go together. The authors Chen et. al [1] consider improving topics by running topic models over several domains and then running a frequent item-set miner to find commonalities between the domains to iteratively enhance the topics. Like [1] they make use of the Generalized Polya urn model which I’ll explore in a another post.

I am not too taken in by the model but the key thing is that the models are evaluated using average topic coherence over held-out data and show an improvement over LDA and other knowledge-based models.


[1] Zhiyuan Chen and Bing Lui. 2014. “Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data”. International Conference on Machine Learning

[2] David M. Mimno and others. 2011. “Optimizing Semantic Coherence in Topic Models”. Empirical Methods in Natural Language Processing

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