Vincenzo Pallotta

Opinion Mining and Sentiment Analysis Need Text Understanding

We argue in this paper that in order to properly capture opinion and sentiment expressed in texts or dialogs any system needs a deep linguistic processing approach. As in other systems, we used ontology matching and concept search, based on standard lexical resources, but a natural language understanding system is still required to spot fundamental and pervasive linguistic phenomena. We implemented these additions to VENSES system and the results of the evaluation are compared to those reported in the state-of-the-art systems in sentiment analysis and opinion mining. We also provide a critical review of the current benchmark datasets as we realized that very often sentiment and opinion is not properly modeled.

Automatic argumentative analysis for interaction mining

Interaction mining is about discovering and extracting insightful information from digital conversations, namely those human–human information exchanges mediated by digital network technology. We present in this article a computational model of natural arguments and its implementation for the automatic argumentative analysis of digital conversations, which allows us to produce relevant information to build interaction business analytics applications overcoming the limitations of standard text mining and information retrieval technology. Applications include advanced visualisations and abstractive summaries.

Smart heating systems: Optimizing heating systems by kinetic-awareness

The greenhouse gas effect has become a major preoccupation worldwide. Heating of buildings and houses is one of the major sources of this effect. We propose to optimize heating control systems by directly integrating the recognition of people activities and context in the process of temperature regulation. We introduce the concept of kinetic-awareness as a way of making heating control system aware of userspsila motion in the physical space in order to adjust temperature according to their current activities.

Advances in Distributed Agent-Based Retrieval Tools

In this chapter we illustrate our vision about the evolution of search engines, dealing with some emerging questions related to the social role of the user on the Web and to the actual approach to access the information. In this scenario, is ever more evident the need to redefine the information paradigm bringing the information to the user and not more the user to the information, with search engines able to provide results without direct questions from users, anticipating their needs. A Web in service of the user, automatically informed by the system with suggested resources related with his life style and his common behavior without the need to ask for them. This approach will be applied to a project named A Semantic Search Engine for a Business Network where the development of a business network creates a point of contact between the academic and the research world and the productive one by the introduction of Natural Language Processing, user profiling, automatic information classification according to users’ personal schemas, contributing in such a way to redefine the vision of information and delineating processes of Human-Machine Interaction.

Robust methods in analysis of natural language data

The automated analysis of natural language data has become a central issue in the design of intelligent information systems. Processing unconstrained natural language data is still considered as an AI-hard task. However, various analysis techniques have been proposed to address specific aspects of natural language. In particular, recent interest has been focused on providing approximate analysis techniques, assuming that when perfect analysis is not possible, partial results may be still very useful.

Special Issue on Robust Methods in Analysis of Natural Language Data

The automated analysis of natural language data has become a central issue in the design of Intelligent Information Systems. The term natural language is intended to cover all the possible modalities of human communication and it is not restricted to written or spoken language. Processing unrestricted natural language is still considered as an AI-hard task. However various analysis techniques have been proposed in order to address specific aspects of natural language. In particular, recent interest has been on providing approximate analysis techniques, assuming that perfect analysis is not possible, but that partial results are still very useful.

A Meta-logical Semantics for Features and Fluents Based on Compositional Operators over Normal Logic Programs

Features & Fluents is a logical framework proposed by Erik Sandewall for reasoning about action and change by means of a logical language called discrete fluent logic (DFL). In this paper we extend the Sandewalls framework for dealing with continuous time and we introduce a knowledge representation language based on a Horn-like fragment of DFL which we called fluent logic programming. A meta-logical semantics is described as the proof-theoretical counterpart of FLP in alternative to the Sandewall original encapsulated semantics. This semantics makes use of composition operators over general logic programs and can be also considered as an attempt of providing a basis for an effective implementation of a proof-system for a meaningful fragment of the Features & Fluents temporal logic.

Interaction mining: The new frontier of customer interaction analytics

In this paper, we present our solution for argumentative analysis of call center conversations in order to provide useful insights for enhancing Customer Interaction Analytics to a level that will enable more qualitative metrics and key performance indicators (KPIs) beyond the standard approach used in Customer Interaction Analytics. These metrics rely on understanding the dynamics of conversations by highlighting the way participants discuss about topics. By doing that we can detect relevant situations such as social behaviors, controversial topics, customer oriented behaviors, and also predict customer satisfaction.

Kinetic User Interfaces for Flexible Mobile Collaboration

In this paper we address the problem of collaborative workflow in mobile environments. Specifically, we present a solution for opportunistic task assignment to mobile users based on location, context and motion awareness. As an example of this approach, we present an application based on the new Kinetic User Interface framework where users participate to dynamic workflow through their displacement in designated active zones.

Weighted Semantic Parsing: A Robust Approach to Interpretation of Natural Language Queries

This paper focuses on a grammar-based approach to semantic interpretation, which combines the notions of robust and weighted parsing. In restricted domains of application in information extraction and natural language speech based information systems this approach shows acceptable performance. We present an overview of our research carried out within the recent project ISIS (Interaction through Speech with Information Systems) where techniques based on the above notions have been applied.