Lecture 20 Topic Modelling

Topic Modelling

• makeingsense of text

  • English Wikipedia: 6M articles
  • Twitter: 500M tweets per day
  • New York Times: 15M articles
  • arXiv: 1M articles
  • What can we do if we want to learn something about these document collections?

• questions

  • What are the less popular topics on Wikipedia?
  • What are the big trends on Twitter in the past month?
  • How do the social issues evolve over time in New York Times from 1900s to 2000s?
  • What are some influential research areas?

• topic models to the rescue

  • Topic models learn common, overlapping themes in a document collection
  • Unsupervised model
    • No labels; input is just the documents!
  • What’s the output of a topic model?
    • Topics: each topic associated with a list of words
    • Topic assignments: each document associated with a list of topics

• what do topics look like

  • A list of words

  • Collectively describes a concept or subject

  • Words of a topic typically appear in the same set of documents in the corpus(words overlapping in documents)

  • Wikipedia topics(broad)

  • Twitter topics(short,conversational)

  • New York Times topics

• applications of topic models

  • Personalised advertising(e.g. types of products bought)
  • Search engine
  • Discover senses of polysemous words(e.g. apple: fruit, company, two different clusters)

A Brief History of Topic Models

• latent semantic analysis

  • LSA: truncate

  • issues

    • Positive and negative values in the $U$ and $V^T$
    • Difficult to interpret(negative values)

• probabilistic LSA

  • based on a probabilistic model to get rid of negative values

  • issues

    • No more negative values!
    • PLSA can learn topics and topic assignment for documents in the train corpus
    • But it is unable to infer topic distribution on new documents
    • PLSA needs to be re-trained for new documents

• latent dirichlet allocation(LDA)

  • Introduces a prior to the document-topic and topicword distribution
  • Fully generative: trained LDA model can infer topics on unseen documents!
  • LDA is a Bayesian version of PLSA

LDA

• LDA

  • Core idea: assume each document contains a mix of topics
  • But the topic structure is hidden (latent)
  • LDA infers the topic structure given the observed words and documents
  • LDA produces soft clusters of documents (based on topic overlap), rather than hard clusters
  • Given a trained LDA model, it can infer topics on new documents (not part of train data)

• input

  • A collection of documents
  • Bag-of-words
  • Good preprocessing practice:
    • Remove stopwords
    • Remove low and high frequency word types
    • Lemmatisation

• output

  • Topics: distribution over words in each topic

  • Topic assignment: distribution over topics in each document

• learning

  • How do we learn the latent topics?

  • Two main family of algorithms:

    • Variational methods
    • Sampling-based methods

  • sampling method (Gibbs)

    1. Randomly assign topics to all tokens in documents

    2. Collect topic-word and document-topic co-occurrence statistics based on the assignments

       • first give some psudo-counts in every cell of two matrix(smoothing,no event is 0)

       • collect co-occurrence statistics

    3. Go through every word token in corpus and sample a new topic:

       • delete current topic assigned to a word

       • update two matrices

       • compute the probability distribution to sample: $P(t_i|w,d)\propto P(t_i|w)P(t_i|d)$ ($P(t_i|w)\to$ topic-word, $P(t_i|d)\to$ document-topic)

         • $P(t_1|w,d)=P(t_1|mouse)\times P(t_1|d_1)=\frac{0.01}{0.01+0.01+2.01}\times \frac{1.1}{1.1+1.1+2.1}$

       • sample randomly based on the probability distribution

    4. Go to step 2 and repeat until convergence

    • when to stop
      • Train until convergence
      • Convergence = model probability of training set becomes stable
      • How to compute model probability?
        • $logP(w_1,w_2,...,w_m)=log{\sum }_{j=0}^{T}P(w_1|t_j)P(t_j|d_{w_1})+...+log{\sum }_{j=0}^{T}P(w_m|t_j)P(t_j|d_{w_m})$
        • m = #word tokens
        • $P(w_1|t_j)\to$ based on the topic-word co-occurrence matrix
        • $P(t_j|d_{w_1})\to$ based on the document-topic co-occurrence matrix
    • infer topics for new documents

      1. Randomly assign topics to all tokens in new/test documents

      2. Update document-topic matrix based on the assignments; but use the trained topic-word matrix (kept fixed)

      3. Go through every word in the test documents and sample topics: $P(t_i|w,d)\propto P(t_i|w)P(t_i|d)$

      4. Go to step 2 and repeat until convergence

    • hyper-parameters

      • $T$: number of topic

      • $\beta$: prior on the topic-word distribution

      • $\alpha$: prior on the document-topic distribution

      • Analogous to k in add-k smoothing in N-gram LM

      • Pseudo counts to initialise co-occurrence matrix:

      • High prior values $\to$ flatter distribution

        • a very very large value would lead to a uniform distribution

      • Low prior values $\to$ peaky distribution

      • $\beta$: generally small (< 0.01)

        • Large vocabulary, but we want each topic to focus on specific themes

      • $\alpha$: generally larger (> 0.1)

        • Multiple topics within a document

Evaluation

• how to evaluate topic models
  • Unsupervised learning $\to$ no labels
  • Intrinsic(内在的，固有的) evaluation:
    • model logprob / perplexity(困惑度，复杂度) on test documents
    • $logL={\sum }_W{\sum }_{T}logP(w|t)P(t|d_w)$
    • $ppl=ex{p}^{\frac{-logL}{W}}$
• issues with perlexity
  • More topics = better (lower) perplexity
  • Smaller vocabulary = better perplexity
    • Perplexity not comparable for different corpora, or different tokenisation/preprocessing methods
  • Does not correlate with human perception of topic quality
  • Extrinsic(外在的) evaluation the way to go:
    • Evaluate topic models based on downstream task
• topic coherence

  • A better intrinsic evaluation method

  • Measure how coherent the generated topics (blue more coherent than red)

  • A good topic model is one that generates more coherent topics

• word intrusion
  • Idea: inject one random word to a topic
    • {farmers, farm, food, rice, agriculture} $\to$ {farmers, farm, food, rice, cat, agriculture}
  • Ask users to guess which is the intruder word
  • Correct guess $\to$ topic is coherent
  • Try guess the intruder word in:
    • {choice, count, village, i.e., simply, unionist}
  • Manual effort; does not scale
• PMI $\approx$ coherence?
  • High PMI for a pair of words $\to$ words are correlated
    • PMI(farm, rice) $\uparrow$
    • PMI(choice, village) $\downarrow$
  • If all word pairs in a topic has high PMI $\to$ topic is coherent
  • If most topics have high PMI $\to$ good topic model
  • Where to get word co-occurrence statistics for PMI?
    • Can use same corpus for topic model
    • A better way is to use an external corpus (e.g. Wikipedia)
• PMI
  • Compute pairwise PMI of top-N words in a topic
    • $PMI(t)={\sum }_{j=2}^N{\sum }_{i=1}^{j-1}log\frac{P(w_i,w_j)}{P(w_i)P(w_j)}$
  • Given topic: {farmers, farm, food, rice, agriculture}
  • Coherence = sum PMI for all word pairs:
    • PMI(farmers, farm) + PMI(farmers, food) + … + PMI(rice, agriculture)
  • variants
    • Normalised PMI
      • $NPMI(t)={\sum }_{j=2}^N{\sum }_{i=1}^{j-1}\frac{log\frac{P(w_i,w_j)}{P(w_i)P(w_j)}}{-logP(w_i,w_j)}$
    • conditional probability (proved not as good as PMI)
      • $LCP(t)={\sum }_{j=2}^N{\sum }_{i=1}^{j-1}log\frac{P(w_i,w_j)}{P(w_i)}$
  • example (PMI tends to favor rarer words, use NPMI to relieve this problem)

Conclusion

• Topic model: an unsupervised model for learning latent concepts in a document collection
• LDA: a popular topic model
  • Learning
  • Hyper-parameters
• How to evaluate topic models?
  • Topic coherence