(2007) compute the semantic similarity using WordNet.

The term pairs with semantic similarity higher than a predefined threshold will be grouped together.

We measure the semantic similarity between two concepts by using cosine distance between their wiki articles, which are represented as the vectors of wiki concepts as well.

For computation efficiency, we calculate semantic similarities between all promising concept pairs beforehand, and then retrieve the value in a Hash table directly.

Merge concepts whose semantic similarity is larger than predefined threshold (0.35 in our experiments) into the one with largest idf.

The Pruning algorithm uses this dictionary to retrieve semantically similar questions.

To retrieve answers for SMS queries that are semantically similar but lexically different from questions in the FAQ corpus we use the Synonym dictionary described in Section 5.2.

Figure 4: Semantically similar SMS and questions