Dimitrios Galanis

SemEval-2015 Task 12: Aspect Based Sentiment Analysis

SemEval-2015 Task 12, a continuation of SemEval-2014 Task 4, aimed to foster research beyond sentenceor text-level sentiment classification towards Aspect Based Sentiment Analysis. The goal is to identify opinions expressed about specific entities (e.g., laptops) and their aspects (e.g., price). The task provided manually annotated reviews in three domains (restaurants, laptops and hotels), and a common evaluation procedure. It attracted 93 submissions from 16 teams.

Generating natural language descriptions from OWL ontologies: the natural OWL system

We present Naturalowl, a natural language generation system that produces texts describing individuals or classes of owl ontologies. Unlike simpler owl verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like Naturalowl, one can publish information in owl on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of Naturalowl, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent linguistic resources are available, Naturalowl produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.

An Open-Source Natural Language Generator for OWL Ontologies and its Use in Protege and Second Life

We demonstrate an open-source natural language generation engine that produces descriptions of entities and classes in English and Greek from OWL ontologies that have been annotated with linguistic and user modeling information expressed in RDF. We also demonstrate an accompanying plug-in for the Protege ontology editor, which can be used to create the ontologys annotations and generate previews of the resulting texts by invoking the generation engine. The engine has been embedded in robots acting as museum tour guides in the physical world and in Second Life; here we demonstrate the latter application.

A Practically Unsupervised Learning Method to Identify Single-Snippet Answers to Definition Questions on the Web

We present a practically unsupervised learning method to produce single-snippet answers to definition questions in question answering systems that supplement Web search engines. The method exploits on-line encyclopedias and dictionaries to generate automatically an arbitrarily large number of positive and negative definition examples, which are then used to train an SVM to separate the two classes. We show experimentally that the proposed method is viable, that it outperforms the alternative of training the system on questions and news articles from TREC, and that it helps the search engine handle definition questions significantly better.

Conflict Cues in Call Center Interactions

The detection of conflict cues in call center interactions may be related to the quality assessment of the services provided, since these cues reveal both speakers’ emotional states and positioning as expressed through complaining and identifying problematic issues on the one hand and managing requests or resolving problems on the other hand. This paper describes a set of emotional and conversational cues associated to conflict as well as a machine learning approach to classify emotional speech units occurring in a call center dataset by employing emotion labels as well as automatically extracted acoustic and additional context-related features.