Advanced techniques for personalized, interactive question answering

Using a computer to answer questions has been a human dream since the beginning of
the digital era. A first step towards the achievement of such an ambitious goal is to deal
with naturallangilage to enable the computer to understand what its user asks.
The discipline that studies the conD:ection between natural language and the represen~
tation of its meaning via computational models is computational linguistics. According
to such discipline, Question Answering can be defined as the task that, given a question
formulated in natural language, aims at finding one or more concise answers in the form
of sentences or phrases.
Question Answering can be interpreted as a sub-discipline of information retrieval
with the added challenge of applying sophisticated techniques to identify the complex
syntactic and semantic relationships present in text. Although it is widely accepted that
Question Answering represents a step beyond standard infomiation retrieval, allowing a
more sophisticated and satisfactory response to the user's information needs, it still shares
a series of unsolved issues with the latter.
First, in most state-of-the-art Question Answering systems, the results are created
independently of the questioner's characteristics, goals and needs. This is a serious limitation
in several cases: for instance, a primary school child and a History student may
need different answers to the questlon: When did, the Middle Ages begin?
Moreover, users often issue queries not as standalone but in the context of a wider
information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces
would encourage and accommodate the submission of multiple related questions
and handle the user's requests for clarification, interactive Question Answering is still at
its early stages:
Furthermore, an i~sue which still remains open in current Question Answering is
that of efficiently answering complex questions, such as those invoking definitions and
descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess
the correctness of answers to such complex questions.
.. These are the central research problems addressed by this thesis, and are solved as
follows.
An in-depth study on complex Question Answering led to the development of classifiers
for complex answers. These exploit a variety of lexical, syntactic and shallow
semantic features to perform textual classification using tree-~ernel functions for Support
Vector Machines.
The issue of personalization is solved by the integration of a User Modelling corn':
ponent within the the Question Answering model. The User Model is able to filter and
fe-rank results based on the user's reading level and interests.
The issue ofinteractivity is approached by the development of a dialogue model and a
dialogue manager suitable for open-domain interactive Question Answering. The utility
of such model is corroborated by the integration of an interactive interface to allow reference
resolution and follow-up conversation into the core Question Answerin,g system and
by its evaluation.
Finally, the models of personalized and interactive Question Answering are integrated
in a comprehensive framework forming a unified model for future Question Answering
research.