Catálogo de publicaciones - libros

Dividing coherent text into a sequence of coherent segments is a challenging task  since different topics/subtopics are often related  to a common theme(s). Based on lexical cohesion, we can keep track of words and their repetitions and break text into segments at points where the lexical chains are weak. However, there exist words that are more or less evenly distributed across a document (called document-dependent or distributional stopwords), making it difficult to separate one segment from another. To minimize the overlaps between segments, we propose two new measures for removing distributional stopwords based on word distribution. Our experimental results show that the new measures are both efficient to compute and effective for improving the segmentation performance of expository text and transcribed lecture text.

Clustering is often considered the most important unsupervised learning problem and several clustering algorithms have been proposed over the years. Many of these algorithms have been tested on classical clustering corpora such as Reuters and 20 Newsgroups in order to determine their quality. However, up to now the relative hardness of those corpora has not been determined. The relative clustering hardness of a given corpus may be of high interest, since it would help to determine whether the usual corpora used to benchmark the clustering algorithms are hard enough. Moreover, if it is possible to find a set of features involved in the hardness of the clustering task itself, specific clustering techniques may be used instead of general ones in order to improve the quality of the obtained clusters. In this paper, we are presenting a study of the specific feature of the vocabulary overlapping among documents of a given corpus. Our preliminary experiments were carried out on three different corpora: the train and test version of the R8 subset of the Reuters collection and a reduced version of the 20 Newsgroups (Mini20Newsgroups). We figured out that a possible relation between the vocabulary overlapping and the F-Measure may be introduced.

Rapid increase in the amount of the digital audio collections demands a generic framework for robust and efficient indexing and retrieval based on the aural content. In this paper we focus our efforts on developing a generic and robust audio-based multimedia indexing and retrieval framework. First an overview for the audio indexing and retrieval schemes with the major limitations and drawbacks are presented. Then the basic innovative properties of the proposed method are justified accordingly. Finally the experimental results and conclusive remarks about the proposed scheme are reported.

The orthography of many resource-scarce languages includes diacritically marked characters. Falling outside the scope of the standard Latin encoding, these characters are often represented in digital language resources as their unmarked equivalents. This renders corpus compilation more difficult, as these languages typically do not have the benefit of large electronic dictionaries to perform diacritic restoration. This paper describes experiments with a machine learning approach that is able to automatically restore diacritics on the basis of local graphemic context. We apply the method to the African languages of Cilubà, Gĩkũyũ, Kĩkamba, Maa, Sesotho sa Leboa, Tshivenda and Yoruba and contrast it with experiments on Czech, Dutch, French, German and Romanian, as well as Vietnamese and Chinese Pinyin.

Sound ontologies need to incorporate source unidentifiable sounds in an adequate and consistent manner. Computational lexical resources like WordNet have either inserted these descriptions into conceptual categories, or make no attempt to organize the terms for these sounds. This work attempts to add structure to linguistic terms for source unidentifiable sounds. Through an analysis of WordNet and a psycho-acoustic experiment we make some preliminary proposal about which features are highly salient for sound classification. This work is essential for interfacing between source unidentifiable sounds and linguistic descriptions of those sounds in computational applications, such as the Semantic Web and robotics.

This paper deals with the treatment of Named Entities (NEs) in Czech. We introduce a two-level NE classification. We have used this classification for manual annotation of two thousand sentences, gaining more than 11,000 NE instances. Employing the annotated data and Machine-Learning techniques (namely the top-down induction of decision trees), we have developed and evaluated a software system aimed at automatic detection and classification of NEs in Czech texts.

Finding emotions in text is an area of research with wide-ranging applications. We describe an emotion annotation task of identifying emotion category, emotion intensity and the words/phrases that indicate emotion in text. We introduce the annotation scheme and present results of an annotation agreement study on a corpus of blog posts. The average inter-annotator agreement on labeling a sentence as emotion or non-emotion was 0.76. The agreement on emotion categories was in the range 0.6 to 0.79; for emotion indicators, it was 0.66. Preliminary results of emotion classification experiments show the accuracy of 73.89%, significantly above the baseline.

Embodied Conversational Agent (ECA) is the user interface metaphor that allows to naturally communicate information during human-computer interaction in synergic modality dimensions, including voice, gesture, emotion, text, etc. Due to its anthropological representation and the ability to express human-like behavior, ECAs are becoming popular interface front-ends for dialog and conversational applications. One important prerequisite for efficient authoring of such ECA-based applications is the existence of a suitable programming language that exploits the expressive possibilities of multimodally blended messages conveyed to the user. In this paper, we present an architecture and interaction language ECAF, which we used for authoring several ECA-based applications. We also provide the feedback from usability testing we carried for user acceptance of several multimodal blending strategies.

This paper reports on the evaluation of a system that allows the use of spoken queries to retrieve information from a textual document collection. First, a large vocabulary continuous speech recognizer transcribes the spoken query into text. Then, an information retrieval engine retrieves the documents relevant to that query. The system works for Spanish language. In order to increase performance, we proposed a two-pass approach based on dynamic adaptation of language models. The system was evaluated using a standard IR test suite from CLEF. Spoken queries were recorded by 10 different speakers. Results showed that the proposed approach outperforms the baseline system: a relative gain in retrieval precision of 5.74%, with a language model of 60,000 words.

It is quite common to use feature extraction methods prior to classification. Here we deal with three algorithms defining uncorrelated features. The first one is the so-called whitening method, which transforms the data so that the covariance matrix becomes an identity matrix. The second method, the well-known Fast Independent Component Analysis (FastICA) searches for orthogonal directions along which the value of the non-Gaussianity measure is large in the whitened data space. The third one, the Whitening-based Springy Discriminant Analysis (WSDA) is a novel method combination, which provides orthogonal directions for better class separation. We compare the effects of the above methods on a real-time vowel classification task. Based on the results we conclude that the WSDA transformation is especially suitable for this task.