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Used a predefined set of answer types and used supervised learning or manually constructed rules
There will always be questions whose answers do not belong to any of the predefined types. “What are tourist attractions in Reims?” The answer could be many different things. Define a catch-all class. Not as effective as the other answer types.
Granularity – if the types are too specific they are difficult to tag. If they are too general, too many candidates might be identified.
Unsupervised method to dynamically construct a probabilistic answer type model
“ What are the tourist attractions in Reims?” We would expect the answers to fit into the context “X is a tourist attraction.” From a corpus we can find words that appeared in this context.
Using the frequency counts of these words in the context, we construct a probabilistic model to compute the probability for a word w to occur in a set of contexts T given an occurrence of w – P(in(w, T) | w).
Parameters of this model are obtained from an automatically parsed, unlabelled corpus. By asking whether a word would occur in a particular context extracted from a question, we avoid explicitly specifying a list of possible answer types.
Word clusters – abstracting a given word to a class of related words. Clustering by Committee (CBC) algorithm. A word may belong to multiple clusters.
Contexts – context in which a word appears imposes constraints on the semantic type of the word. Represented by undirected paths in the dependency trees involving the word at the beginning or end. The word itself is replaced by X. A word is said to be filler of the context if it replaces X.
Question contexts are extracted from a question. An answer is a plausible filler of a question context. Two rules – if wh-word has a trace in the parse tree, the question contexts are the contexts of the trace. If wh-word is a determiner, then single context involving the noun that is modified by the determiner.
Candidate contexts are extracted from parse trees of the candidate answers. Occurrence of washington in 'Washington's descendants' and 'suburban Washington' should be scored differently if the question is seeking a location.
Goal is to evaluate the appropriateness of an answer candidate. This is proportional to the probability that it will be a filler of the question context Tq extracted from the question. P(in(w, Tq)|w).
To mitigate data sparseness, variable C for clusters is introduced. It can be shown that the above model splits into two parts – one that models which clusters a word belongs to and the other that models the appropriateness of the cluster to question contexts.
Then introduce the candidate context and compute joint likelihood P(in(w,Tq) | w, in(w, Tw)), where Tw is the set of contexts for the candidate w.
Query – flow graph: Nodes represent unique queries and two nodes are connected by a directed edge if they occur consecutively in a search session. A weighing function assigns weight representing the probability that two nodes q and q' are part of the same chain.
Chain is defined as a sequence of queries with similar information need.