6,919 research outputs found
Information Retrieval Models
Many applications that handle information on the internet would be completely\ud
inadequate without the support of information retrieval technology. How would\ud
we find information on the world wide web if there were no web search engines?\ud
How would we manage our email without spam filtering? Much of the development\ud
of information retrieval technology, such as web search engines and spam\ud
filters, requires a combination of experimentation and theory. Experimentation\ud
and rigorous empirical testing are needed to keep up with increasing volumes of\ud
web pages and emails. Furthermore, experimentation and constant adaptation\ud
of technology is needed in practice to counteract the effects of people that deliberately\ud
try to manipulate the technology, such as email spammers. However,\ud
if experimentation is not guided by theory, engineering becomes trial and error.\ud
New problems and challenges for information retrieval come up constantly.\ud
They cannot possibly be solved by trial and error alone. So, what is the theory\ud
of information retrieval?\ud
There is not one convincing answer to this question. There are many theories,\ud
here called formal models, and each model is helpful for the development of\ud
some information retrieval tools, but not so helpful for the development others.\ud
In order to understand information retrieval, it is essential to learn about these\ud
retrieval models. In this chapter, some of the most important retrieval models\ud
are gathered and explained in a tutorial style
Eliciting Expertise
Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general
Parsing of Spoken Language under Time Constraints
Spoken language applications in natural dialogue settings place serious
requirements on the choice of processing architecture. Especially under adverse
phonetic and acoustic conditions parsing procedures have to be developed which
do not only analyse the incoming speech in a time-synchroneous and incremental
manner, but which are able to schedule their resources according to the varying
conditions of the recognition process. Depending on the actual degree of local
ambiguity the parser has to select among the available constraints in order to
narrow down the search space with as little effort as possible.
A parsing approach based on constraint satisfaction techniques is discussed.
It provides important characteristics of the desired real-time behaviour and
attempts to mimic some of the attention focussing capabilities of the human
speech comprehension mechanism.Comment: 19 pages, LaTe
Model-based Bayesian Reinforcement Learning for Dialogue Management
Reinforcement learning methods are increasingly used to optimise dialogue
policies from experience. Most current techniques are model-free: they directly
estimate the utility of various actions, without explicit model of the
interaction dynamics. In this paper, we investigate an alternative strategy
grounded in model-based Bayesian reinforcement learning. Bayesian inference is
used to maintain a posterior distribution over the model parameters, reflecting
the model uncertainty. This parameter distribution is gradually refined as more
data is collected and simultaneously used to plan the agent's actions. Within
this learning framework, we carried out experiments with two alternative
formalisations of the transition model, one encoded with standard multinomial
distributions, and one structured with probabilistic rules. We demonstrate the
potential of our approach with empirical results on a user simulator
constructed from Wizard-of-Oz data in a human-robot interaction scenario. The
results illustrate in particular the benefits of capturing prior domain
knowledge with high-level rules
Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches
In dialogical argumentation it is often assumed that the involved parties
always correctly identify the intended statements posited by each other,
realize all of the associated relations, conform to the three acceptability
states (accepted, rejected, undecided), adjust their views when new and correct
information comes in, and that a framework handling only attack relations is
sufficient to represent their opinions. Although it is natural to make these
assumptions as a starting point for further research, removing them or even
acknowledging that such removal should happen is more challenging for some of
these concepts than for others. Probabilistic argumentation is one of the
approaches that can be harnessed for more accurate user modelling. The
epistemic approach allows us to represent how much a given argument is believed
by a given person, offering us the possibility to express more than just three
agreement states. It is equipped with a wide range of postulates, including
those that do not make any restrictions concerning how initial arguments should
be viewed, thus potentially being more adequate for handling beliefs of the
people that have not fully disclosed their opinions in comparison to Dung's
semantics. The constellation approach can be used to represent the views of
different people concerning the structure of the framework we are dealing with,
including cases in which not all relations are acknowledged or when they are
seen differently than intended. Finally, bipolar argumentation frameworks can
be used to express both positive and negative relations between arguments. In
this paper we describe the results of an experiment in which participants
judged dialogues in terms of agreement and structure. We compare our findings
with the aforementioned assumptions as well as with the constellation and
epistemic approaches to probabilistic argumentation and bipolar argumentation
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