Carlo Strapparava

Adaptive Hypermedia and Adaptive Web-Based Systems

Now welcome, the most inspiring book today from a very professional writer in the world, adaptive hypermedia and adaptive web based systems 5th international conference ah 2008 hannover germany july 29 august 1 2008 proceedings lecture notes in computer science. This is the book that many people in the world waiting for to publish. After the announced of this book, the book lovers are really curious to see how this book is actually. Are you one of them? Thats very proper. You may not be regret now to seek for this book to read.

Learning to identify emotions in text

This paper describes experiments concerned with the automatic analysis of emotions in text. We describe the construction of a large data set annotated for six basic emotions: ANGER, DISGUST, FEAR, JOY, SADNESS and SURPRISE, and we propose and evaluate several knowledge-based and corpusbased methods for the automatic identification of these emotions in text.

The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language

In this paper, we present initial experiments in the recognition of deceptive language. We introduce three data sets of true and lying texts collected for this purpose, and we show that automatic classification is a viable technique to distinguish between truth and falsehood as expressed in language. We also introduce a method for class-based feature analysis, which sheds some light on the features that are characteristic for deceptive text.

The role of domain information in Word Sense Disambiguation

This paper explores the role of domain information in word sense disambiguation. The underlying hypothesis is that domain labels, such as MEDICINE, ARCHITECTURE and SPORT, provide a useful way to establish semantic relations among word senses, which can be profitably used during the disambiguation process. Results obtained at the SENSEVAL-2 initiative confirm that for a significant subset of words domain information can be used to disambiguate with a very high level of precision.

LEARNING TO LAUGH (AUTOMATICALLY): COMPUTATIONAL MODELS FOR HUMOR RECOGNITION

Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this article, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non‐humorous texts, with significant improvements observed over a priori known baselines.

Making Computers Laugh: Investigations in Automatic Humor Recognition

Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this paper, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines.

Modelling and Adapting to Context

Abstract: One of the hardest points in context-aware applications is deciding what reactions a system has to a certain context. In this paper, we introduce an architecture used in two context-aware museum guides. We discuss how the context is modelled and we briefly present a rule-based mechanism to trigger system actions. A rule-based system offers the flexibility required to be context-sensitive in the broadest sense since many context features can be considered and evaluated at the same time. This architecture is very flexible and easily supports a fast prototyping approach.

Unsupervised and supervised exploitation of semantic domains in lexical disambiguation

Abstract Domains are common areas of human discussion, such as economics, politics, law, science, etc., which are at the basis of lexical coherence. This paper explores the dual role of domains in word sense disambiguation (WSD). On one hand, domain information provides generalized features at the paradigmatic level that are useful to discriminate among word senses. On the other hand, domain distinctions constitute a useful level of coarse grained sense distinctions, which lends itself to more accurate disambiguation with lower amounts of knowledge. In this paper we extend and ground the modeling of domains and the exploitation of WordNet Domains , an extension of WordNet in which each synset is labeled with domain information. We propose a novel unsupervised probabilistic method for the critical step of estimating domain relevance for contexts, and suggest utilizing it within unsupervised domain driven disambiguation for word senses, as well as within a traditional supervised approach. The paper presents empirical assessments of the potential utilization of domains in WSD at a wide range of comparative settings, supervised and unsupervised. Following the dual role of domains we report experiments that evaluate both the extent to which domain information provides effective features for WSD, as well as the accuracy obtained by WSD at domain-level sense granularity. Furthermore, we demonstrate the potential for either avoiding or minimizing manual annotation thanks to the generalized level of information provided by domains.

Exploiting Comparable Corpora and Bilingual Dictionaries for Cross-Language Text Categorization

Cross-language Text Categorization is the task of assigning semantic classes to documents written in a target language (e.g. English) while the system is trained using labeled documents in a source language (e.g. Italian).In this work we present many solutions according to the availability of bilingual resources, and we show that it is possible to deal with the problem even when no such resources are accessible. The core technique relies on the automatic acquisition of Multilingual Domain Models from comparable corpora.Experiments show the effectiveness of our approach, providing a low cost solution for the Cross Language Text Categorization task. In particular, when bilingual dictionaries are available the performance of the categorization gets close to that of monolingual text categorization.

Cross Language Text Categorization by Acquiring Multilingual Domain Models from Comparable Corpora

In a multilingual scenario, the classical monolingual text categorization problem can be reformulated as a cross language TC task, in which we have to cope with two or more languages (e.g. English and Italian). In this setting, the system is trained using labeled examples in a source language (e.g. English), and it classifies documents in a different target language (e.g. Italian). In this paper we propose a novel approach to solve the cross language text categorization problem based on acquiring Multilingual Domain Models from comparable corpora in a totally unsupervised way and without using any external knowledge source (e.g. bilingual dictionaries). These Multilingual Domain Models are exploited to define a generalized similarity function (i.e. a kernel function) among documents in different languages, which is used inside a Support Vector Machines classification framework. The results show that our approach is a feasible and cheap solution that largely outperforms a baseline.