  Paraphrases are important linguistic resources for a wide variety of NLP
applications. Many techniques for automatic paraphrase mining from general
corpora have been proposed. While these techniques are successful at
discovering generic paraphrases, they often fail to identify domain-specific
paraphrases (e.g., {staff, concierge} in the hospitality domain). This is
because current techniques are often based on statistical methods, while
domain-specific corpora are too small to fit statistical methods. In this
paper, we present an unsupervised graph-based technique to mine paraphrases
from a small set of sentences that roughly share the same topic or intent. Our
system, Essentia, relies on word-alignment techniques to create a
word-alignment graph that merges and organizes tokens from input sentences. The
resulting graph is then used to generate candidate paraphrases. We demonstrate
that our system obtains high-quality paraphrases, as evaluated by crowd
workers. We further show that the majority of the identified paraphrases are
domain-specific and thus complement existing paraphrase databases.
