(The above sentences are extracted from WebMD forum discussions that are of Speech Act type 'disclose')
How does a Computer make sense out of People's Natural Language conversations so that it can augment the conversational experience by bringing in relevant information? As it turns out, even the problem of generating meaningful and contextual queries from conversations isn't trivial, especially if you want to avoid deep NLP (parsing, SRL) techniques (for reasons such as fast response times given the resources).
This diagram shows a part of Cobot conversational search system that bootstraps on ontological terms, dictionary entries and shallow Chunk phrases to build possible query term candidates using Augmented Transition Networks(ATNs). ATNs are recursive Finite State Machines that consult other machines during intermediate state steps for walking over words, chunks and sentences to extract information for which the machines have been encoded.
The advantages of using these machines is that we can easily add states and other machines to add complex extraction patterns from text. However, this makes the machines hard to manipulate in future as the complexity of encodings increases.
I am extracting the following simple templates from sentences to extract some candidates:
VP PP ChunkNP-DictTerm (eg. start with alcoholism)
VP ChunkNP-DictTerm PP NP VP NP (find a psychiatrist with experience treating adult ADHD and substance abuse problems.)
VP ChunkNP-DictTerm (quit smoking)
After generating several possible query candidates, the Snippet Analyzer stage determines the Speech Acts in sentences and applies several Query Generation Strategies to compose queries for different search engines and other document repositories. For example, if a user has asked a Question, the Query Generator applies question based templates to generate some queries. Similarly, if a user has given some advice to some other user in the conversation, the engine tries to generate queries to check the validity of advice and how to act on the advice, for example.
