A Context-theoretic Framework for Compositionality in Distributional Semantics
Table 2: Results obtained with our Latent Dirichlet projection model on the data from the first Recognising Textual Entailment Challenge for two document lengths N=106 and N=107 using a cut-off for the degree of entailment of 0.5 at which entailment was regarded as holding.
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] CWS']
We built a latent Dirichlet allocation model using \nameciteBlei:03’s implementation on documents from the British National Corpus, using 100 topics. We evaluated this model on the 800 entailment pairs from the first Recognising Textual Entailment Challenge test set. The differences between the accuracy values in the table are not statistically significant because of the small dataset, although all accuracies in the table are significantly better than chance at the 1% level. We experimented with various document lengths and found very long documents (N=106 and N=107) to work best.