Learning to Disambiguate Potentially Subjective Expressions

Learning to Disambiguate Potentially Subjective Expressions

Learning Extraction Patterns for Subjective Expressions

This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping proces...

Linguistic Cues for Distinguishing Literal and Non-Literal Usages

We investigate the effectiveness of different linguistic cues for distinguishing literal and non-literal usages of potentially idiomatic expressions. We focus specifically on features that generalize across different target expressions. While idioms on the whole are frequent, instances of each particular expression can be relatively infrequent and it will often not be feasible to extract and an...

Bsmooth: Learning from user feedback to disambiguate query terms in interactive data retrieval

There is great interest in supporting imprecise queries over databases today. To support such queries, the system is typically required to disambiguate parts of the userspecified query against the database, using whatever resources are intrinsically available to it (the database schema, value distributions, natural language models etc). Often, systems will also have a user-interaction log avail...

Exploring the effects of gaze and pauses in situated human-robot interaction

In this paper, we present a user study where a robot instructs a human on how to draw a route on a map, similar to a Map Task. This setup has allowed us to study user reactions to the robot’s conversational behaviour in order to get a better understanding of how to generate utterances in incremental dialogue systems. We have analysed the participants' subjective rating, task completion, verbal ...