In order to compare the performance of the learned policy with hand-coded REG policies, three simple rule-based policies were built.

The results show that using our RL framework, REG policies can be learned using data-driven simulations, and that such a policy can predict and adapt to a user’s knowledge pattern more accurately than policies trained using hand-coded rule-based simulations and hand-coded baseline policies.

Rule-based and superVised learning approaches to user adaptation in SDS have been proposed earlier (Cawsey, 1993; Akiba and Tanaka, 1994).

We also compared the performance of policies learned using a hand-coded rule-based simulation and a data-driven statistical simulation and show that data-driven simulations produce better policies than rule-based ones.

Rule-based and supervised learning approaches have been proposed to learn and adapt during the conversation dynamically.

It is also not clear how supervised and rule-based approaches choose between when to seek more information and when to adapt.

Earlier, we reported a proof-of-concept work using a hand-coded rule-based user simulation (J anarthanam and Lemon, 2009c).

We used two kinds of action selection models: corpus-driven statistical model and hand-coded rule-based model.

5.2 Rule-based action selection model

We also built a rule-based simulation using the above models but where some of the parameters were set manually instead of estimated from the data.

As information extraction (IE) becomes more central to enterprise applications, rule-based IE engines have become increasingly important.

In this paper, we describe SystemT, a rule-based IE system whose basic design removes the expressivity and performance limitations of current systems based on cascading grammars.

In recent years, these systemic requirements have led to renewed interest in rule-based IE systems (Doan et al., 2008; SAP, 2010; IBM, 2010; SAS, 2010).

Until recently, rule-based IE systems (Cunningham et al., 2000; Boguraev, 2003; Drozdzynski et al., 2004) were predominantly based on the cascading grammar formalism exemplified by the

Compared to the within-event-type rules, the cross-event model yields much more improvement for trigger classification: rule-based propagation gains 1.7% improvement while the cross-event model achieves a further 7.3% improvement.

For argument and role classification, the cross-event model also gains 3% and 2.3% above that obtained by the rule-based propagation process.

Ji and Grishman (2008) were inspired from the hypothesis of “One Sense Per Discourse” (Yarowsky, 1995); they extended the scope from a single document to a cluster of topic-related documents and employed a rule-based approach

We will argue that the automatic identification of generic expressions should be cast as a machine learning problem instead of a rule-based approach, as there is (i) no transparent marking of genericity in English (as in most other European languages) and (ii) the phenomenon is highly context dependent.

Suh (2006) applied a rule-based approach to automatically identify generic noun phrases.

Lexical semantic factors, such as the semantic type of the clause predicate (5.c,e), or “well-established” kinds (5.g) may favour a generic reading, but such lexical factors are difficult to capture in a rule-based setting.