The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

The study of computational agents capable of rational behaviour has received a great deal of attention in recent years. Theoretical formalizations of such agents and their implementations have proceeded in parallel with little or no connection between them. Tkis paper explores a particular type of rational agent, a BeliefDesire-Intention (BDI) agent. The primary aim of this paper is to integrate (a) the theoretical foundations of BDI agents from both a quantitative decision-theoretic perspective and a symbolic reasoning perspective; (b) the implementations of BDI agents from an ideal theoretical perspective and a more practical perspective; and (c) the building of large-scale applications based on BDI agents. In particular, an air-trafflc management application will be described from both a theoretical and an implementation perspective.

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BDI Agents: From Theory to Practice

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

Lexicon-based methods for sentiment analysis

This paper examines a particular PROLOG implementation of Discourse Representation theory (DR theory) constructed at the University of Texas. The implementation also contains a Lexical Functional Grammar parser that provides f-structures: these f-structures are then translated into the semantic representations posited by DR theory, structures which are known as Discourse Representation Structures (DRSs). Our program handles some linguistically interesting phenomena in English such as (i) scope ambiguities of singular quantifiers, (ii) functional control phenomena, and (iii) long distance dependencies. Finally, we have implemented an algorithm for anaphora resolution. Our goal is to use purely linguistically available information in constructing a semantic representation of discourse as far as is feasible and to forego appeals to world knowledge.

BUILDRS: an implementation of DR theory and LFG

Cet article decrit la ressource ANNODIS, issue d'un projet ﬁnance par l'ANR, corpus de francais ecrit enrichi a differents niveaux, dont un niveau d'annotation manuelle de structures discursives. Une originalite de la ressource est de proposer un corpus diversiﬁe (plusieurs types de textes sont representes) et deux annotations fondees sur des approches distinctes de la structuration des discours. La description de la ressource - objets annotes, textes composant le corpus - s'accompagne de la presentation des ancrages theoriques sous-jacents aux modeles d'annotation, et des choix methodologiques qui ont guide les diverses phases de preparation et d'annotation du corpus. Nous formulons les enjeux d'une telle ressource pour la linguistique et le TAL, et presentons les premieres exploitations.

https://halshs.archives-ouvertes.fr/halshs-00935201/document

La ressource ANNODIS, un corpus enrichi d'annotations discursives

While linguists and philosophers have sought to model the various ways in which the meaning of what we say can depend on the nonlinguistic context, this work has by and large focused on how the nonlinguistic context can be exploited to ground or anchor referential or otherwise context-sensitive expressions. In this paper, we focus on examples in which nonlinguistic events contribute entire discourse units that serve as arguments to coherence relations, without the mediation of context-sensitive expressions. We use both naturally occurring and constructed examples to highlight these interactions and to argue that extant coherence-based accounts of discourse should be extended to model them. We also argue that extending coherence-based accounts in this way is a nontrivial task. It forces us to reassess basic notions of the nonlinguistic context and rhetorical relations as well as models of discourse structure, evolution, and interpretation. Our paper addresses the conceptual and technical revisions that these types of interaction demand.

/pdf/a-formal-semantics-for-situated-conversation-3l9ddkgr4j.pdf

A formal semantics for situated conversation

A Web of Words: Lexical Meaning in Context

In this article, we explore an integration of a formal semantic approach to lexical meaning and an approach based on distributional methods. First, we outline a formal semantic theory that aims to combine the virtues of both formal and distributional frameworks. We then proceed to develop an algebraic interpretation of that formal semantic theory and show how at least two kinds of distributional models make this interpretation concrete. Focusing on the case of adjective-noun composition, we compare several distributional models with respect to the semantic information that a formal semantic theory would need, and we show how to integrate the information provided by distributional models back into the formal semantic framework.

/pdf/integrating-type-theory-and-distributional-semantics-a-case-5666rcluft.pdf

Nicholas Asher

Papers

BUILDRS: an implementation of DR theory and LFG

La ressource ANNODIS, un corpus enrichi d'annotations discursives

A formal semantics for situated conversation

A Web of Words: Lexical Meaning in Context

Integrating type theory and distributional semantics: A case study on adjective-noun compositions