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This paper proposes a novel representation for Authorship Attribution (AA), based on Concise Semantic Analysis (CSA), which has been successfully used in Text Categorization (TC). Our approach for AA, called Document Author Representation (DAR), builds document vectors in a space of authors, calculating the relationship between textual features and authors. In order to evaluate our approach, we compare the proposed representation with conventional approaches and previous works using the c50 corpus. We found that DAR can be very useful in AA tasks, because it provides good performance on imbalanced data, getting comparable or better accuracy results.

The traditional approach for spoken document retrieval (SDR) uses an automatic speech recognizer (ASR) in combination with a word-based information retrieval method. This approach has only showed limited accuracy, partially because ASR systems tend to produce transcriptions of spontaneous speech with significant word error rate. In order to overcome such limitation we propose a method which uses word and phonetic-code representations in collaboration. The idea of this combination is to reduce the impact of transcription errors in the processing of some (presumably complex) queries by representing words with similar pronunciations through the same phonetic code. Experimental results on the CLEF-CLSR-2007 corpus are encouraging; the proposed hybrid method improved the mean average precision and the number of retrieved relevant documents from the traditional word-based approach by 3% and 7% respectively.

The acquisition of naturalistic speech data and the richness of its annotation are very important to face the challenges of automatic emotion recognition from speech. This paper describes the creation of a database of emotional speech in the Spanish spoken in Mexico. It was recorded from children between 7 and 13 years old while playing a sorting card game with an adult examiner. The game is based on a neuropsychological test, modified to encourage dialogue and induce emotions in the player. The audio was segmented at speaker turn level and annotated with six emotional categories and three continuous emotion primitives by 11 human evaluators. Inter-evaluator agreement is presented for categorical and continuous annotation. Initial classification and regression experiments were performed using a set of 6,552 acoustic features.

Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to provide domain knowledge with extra rewards to converge faster. The reward shaping functions are normally defined in advance by the user and are static. This paper introduces a dynamic reward shaping approach, in which these extra rewards are not consistently given, can vary with time and may sometimes be contrary to what is needed for achieving a goal. In the experiments, a user provides verbal feedback while a robot is performing a task which is translated into additional rewards. It is shown that we can still guarantee convergence as long as most of the shaping rewards given per state are consistent with the goals and that even with fairly noisy interaction the system can still produce faster convergence times than traditional reinforcement learning techniques.

Crosslingual text classification consists of exploiting labeled documents in a source language to classify documents in a different target language. In addition to the evident translation problem, this task also faces some difficulties caused by the cultural discrepancies manifested in both languages by means of different topic distributions. Such discrepancies make the classifier unreliable for the categorization task. In order to tackle this problem we propose to improve the classification performance by using information embedded in the own target dataset. The central idea of the proposed approach is that similar documents must belong to the same category. Therefore, it classifies the documents by considering not only their own content but also information about the assigned category to other similar documents from the same target dataset. Experimental results using three different languages evidence the appropriateness of the proposed approach.

Current text classification methods are mostly based on a supervised approach, which require a large number of examples to build models accurate. Unfortunately, in several tasks training sets are extremely small and their generation is very expensive. In order to tackle this problem in this paper we propose a new text classification method that takes advantage of the information embedded in the own test set. This method is supported on the idea that similar documents must belong to the same category. Particularly, it classifies the documents by considering not only their own content but also information about the assigned category to other similar documents from the same test set. Experimental results in four data sets of different sizes are encouraging. They indicate that the proposed method is appropriate to be used with small training sets, where it could significantly outperform the results from traditional approaches such as Naive Bayes and Support Vector Machines.

Most common feature selection techniques for document categorization are supervised and require lots of training data in order to accurately capture the descriptive and discriminative information from the defined categories. Considering that training sets are extremely small in many classification tasks, in this paper we explore the use of unsupervised extractive summarization as a feature selection technique for document categorization. Our experiments using training sets of different sizes indicate that text summarization is a competitive approach for feature selection, and show its appropriateness for situations having small training sets, where it could clearly outperform the traditional information gain technique.

Geographic Information Retrieval (GIR) is a specialized Information Retrieval (IR) branch that deals with information related to geographical locations. Traditional IR engines are perfectly able to retrieve the majority of the relevant documents for most geographical queries, but they have severe difficulties generating a pertinent ranking of the retrieved results, which leads to poor performance. A key reason for this ranking problem has been a lack of information. Therefore, previous GIR research has tried to fill this gap using robust geographical resources (i.e. a geographical ontology), while other research with the same aim has used relevant feedback techniques instead. This paper explores the use of Bag of Concepts (BoC; a representation where documents are considered as the union of the meanings of its terms) and Holographic Reduced Representation (HRR; a novel representation for textual structure) as re-ranking mechanisms for GIR. Our results reveal an improvement in mean average precision (MAP) when compared to the traditional vector space model, even if Pseudo Relevance Feedback is employed.

The full deployment of service robots in daily activities will require the robot to adapt to the needs of non-expert users, particularly, to learn how to perform new tasks from "natural" interactions. Reinforcement learning has been widely used in robotics, however, traditional algorithms require long training times, and may have problems with continuous spaces. Programming by demonstration has been used to instruct a robot, but is limited by the quality of the trace provided by the user. In this paper, we introduce a novel approach that can handle continuous spaces, can produce continuous actions and incorporates the user's intervention to quickly learn optimal policies of tasks defined by the user. It is shown how the continuous actions produce smooth trajectories and how the user's intervention allows the robot to learn significantly faster optimal policies. The proposed approach is tested in a simulated robot with very promising results.

This paper describes experimental results of two approaches to multimedia image retrieval: annotation-based expansion and late fusion of mixed methods. The former formulation consists of expanding manual annotations with labels generated by automatic annotation methods. Experimental results show that the performance of text-based methods can be improved with this strategy, specially, for visual topics; motivating further research in several directions. The second approach consists of combining the outputs of diverse image retrieval models based on different information. Experimental results show that competitive performance, in both retrieval and results diversification, can be obtained with this simple strategy. It is interesting that, contrary to previous work, the best results of the fusion were obtained by assigning a high weight to visual methods. Furthermore, a probabilistic modeling approach to result-diversification is proposed; experimental results reveal that some modifications are needed to achieve satisfactory results with this method.

As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using a Web-based approach for determining the correct sense of an ambiguous word based only in its surrounding context. In particular, we propose a semi-supervised method that is specially suited to work with just a few training examples. The method considers the automatic extraction of unlabeled examples from the Web and their iterative integration into the training data set. The experimental results, obtained over a subset of ten nouns from the SemEval lexical sample task, are encouraging. They showed that it is possible to improve the baseline accuracy of classifiers such as Naive Bayes and SVM using some unlabeled examples extracted from the Web.

This year we evaluated our supervised answer validation method at both, the Spanish Answer Validation Exercise (AVE) and the Spanish Question Answering Main Task. This paper describes and analyzes our evaluation results from both tracks. In resume, the F-measure of the proposed method outperformed the baseline result of the AVE 2008 task by more than 100%, and enhanced the performance of our question answering system, showing a gain in accuracy of 22% for answering factoid questions. A detailed analysis of the results shows that the proposed non-overlap features are most discriminative than the traditional overlap ones. In particular, these novel features allowed increasing the F-measure result of our method by 26%.

Cross-language text classification (CLTC) aims to take advantage of existing training data from one language to construct a classifier for another language. In addition to the expected translation issues, CLTC is also complicated by the cultural distance between both languages, which causes that documents belonging to the same category concern very different topics. This paper proposes a re-classification method which purpose is to reduce the errors caused by this phenomenon by considering information from the own target language documents. Experimental results in a news corpus considering three pairs of languages and four categories demonstrated the appropriateness of the proposed method, which could improve the initial classification accuracy by up to 11%.

This paper focuses on the problem of ranking documents for Geographic Information Retrieval. It aims to demonstrate that by using some query-related example texts it is possible to improve the final ranking of the retrieved documents. Experimental results indicated that our approach could improve the MAP of some sets of retrieved documents using only two example texts.

Recent evaluation results from Geographic Information Retrieval (GIR) indicate that current information retrieval methods are effective to retrieve relevant documents for geographic queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results in this paper we present a novel re-ranking method, which employs information obtained through a relevance feedback process to perform a ranking refinement . Performed experiments show that the proposed method allows to improve the generated ranking from a traditional IR machine, as well as results from traditional re-ranking strategies such as query expansion via relevance feedback.

Some recent works have shown that the "perfect" selection of the best IR system per query could lead to a significant improvement on the retrieval performance. Motivated by this fact, in this paper we focus on the automatic selection of the best retrieval result from a given set of results lists generated by different IR systems. In particular, we propose five heuristic measures for evaluating the relative relevance of each result list, which take into account the redundancy and ranking of documents across the lists. Preliminary results in three different data sets, and considering 216 queries, are encouraging. They show that the proposed approach could slightly outperform the results from the best individual IR system in two out of three collections, but that it could significantly improve the average results of individual systems from all data sets. In addition, the achieved results indicate that our approach is a competitive alternative to traditional data fusion methods.

The bag of words representation (BoW), which is widely used in information retrieval (IR), represents documents and queries as word lists that do not express anything about context information. When we look for information, we find that not everything is explicitly stated in a document, so context information is needed to understand its content. This paper proposes the use of bag of concepts (BoC) and Holographic reduced representation (HRR) in IR. These representations go beyond BoW by incorporating context information to document representations. Both HRR and BoC are produced using a vector space methodology known as Random Indexing, and allow expressing additional knowledge from different sources. Our experiments have shown the feasibility of the representations and improved the mean average precision by up to 7% when they are compared with the traditional vector space model.

This paper describes the results of the INAOE's answer va-lidation system evaluated at the Spanish track of the AVE 2007. The system is based on a supervised learning approach that considers two kinds of attributes. On the one hand, some attributes indicating the textual entailment between the given support text and the hypothesis constructed from the question and answer. On the other hand, some new features denoting certain answer restrictions as imposed by the question's type and format. The evaluation results were encouraging; they reached a F-measure of 53% (the best performance in the Spanish track), and outperformed the standard baseline by 15 percentage points.

This paper discusses our system's results at the Spanish Question Answering task of CLEF 2007. Our system is centered in a full data-driven approach that combines information retrieval and machine learning techniques. It mainly relies on the use of lexical information and avoids any complex language processing procedure. Evaluation results indicate that this approach is very effective for answering definition questions from Wikipedia. In contrast, they also reveal that it is very difficult to respond factoid questions from this resource solely based on the use of lexical overlaps and redundancy.

Nowadays there exist several kinds of question answering systems. According to recent evaluation results, most of these systems are complementary (i.e., each one is better than the others in answering some specific type of questions). This fact indicates that a pertinent combination of various systems may allow improving the best individual result. This paper focuses on this problem. It proposes using an answer validation method to handle this combination. The main advantage of this approach is that it does not rely on internal system's features nor depend on external answer's redundancies. Experimental results confirm the appropriateness of our proposal. They mainly show that it outperforms individual system's results as well as the precision obtained by a redundancy-based combination strategy.

Automatic call routing is one of the most important issues in the call center domain. It can be modeled ---once performed the speech recognition of utterances--- as a text classification task. Nevertheless, in this case, texts are extremely small (just a few words) and there are a great number of narrow call-type classes. In this paper, we propose a text classification method specially suited to work on this scenario. This method considers a new weighting scheme of terms and uses a multiple stage classification approach with the aim of balance the rate of rejected calls (directed to a human operator) and the classification accuracy. The proposed method was evaluated on a Spanish corpus consisting of 24,638 call utterances achieving outstanding results: 95.5% of classification accuracy with a rejection rate of just 8.2%.

Current storage and processing facilities have caused the emergence of many multimedia repositories and, consequently, they have also triggered the necessity of new approaches for information retrieval. In particular, spoken document retrieval is a very complex task since existing speech recognition systems tend to generate several transcription errors (such as word substitutions, insertions and deletions). In order to deal with these errors, this paper proposes an enriched document representation based on a phonetic codification of the automatic transcriptions. This representation aims to reduce the impact of the transcription errors by representing words with similar pronunciations through the same phonetic code. Experimental results on the CL-SR corpus from the CLEF 2007 (which includes 33 test topics and 8,104 English interviews) are encouraging; our method achieved a mean average precision of 0.0795, outperforming all except one of the evaluated systems at this forum.

In this paper we report results of experiments conducted with strategies for improving text-based image retrieval. The adopted strategies were evaluated in the photographic retrieval task at ImageCLEF2007. We propose a Web-based method for expanding textual queries with related terms. This technique was the top-ranked query expansion method among those proposed by other ImageCLEF2007 participants. We also consider two methods for combining visual and textual information in the retrieval process: late-fusion and intermedia-feedback. The best results were obtained by combining intermedia-feedback and our expansion technique. The main contribution of this paper, however, is the proposal of "annotation-based expansion"; a novel approach that consists of using labels assigned to images (with image annotation methods) for expanding textual queries and documents. We introduce this idea and report results of initial experiments towards enhancing text-based image retrieval via image annotation. Preliminary results show that this expansion strategy could be useful for image retrieval in the near future.

As any other text categorization task, authorship attribution requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using Web-based text mining methods for the identification of the author of a given poem. In particular, we propose a semi-supervised method that is specially suited to work with justfew training examples in order to tackle the problem of the lack of data with the same writing style. The method considers the automatic extraction of the unlabeled examples from the Web and its iterative integration into the training data set. To the knowledge of the authors, a semi-supervised method which makes use of the Web as support lexical resource has not been previously employed in this task. The results obtained on poem categorization show that this method may improve the classification accuracy and it is appropriate to handle the attribution of short documents.

A problem of supervised approaches for text classification is that they commonly require high-quality training data to construct an accurate classifier. Unfortunately, in many real-world applications the training sets are extremely small and present imbalanced class distributions. In order to confront these problems, this paper proposes a novel approach for text classification that combines under-sampling with a semi-supervised learning method. In particular, the proposed semi-supervised method is specially suited to work with very few training examples and considers the automatic extraction of untagged data from the Web. Experimental results on a subset of Reuters-21578 text collection indicate that the proposed approach can be a practical solution for dealing with the class-imbalance problem, since it allows achieving very good results using very small training sets.

This paper describes a method for extracting hyponyms from free text. In particular it explores two main matters. On the one hand, the possibility of reaching favorable results using only lexical extraction patterns. On the other hand, the usefulness of measuring the instance's confidences based on the pattern's confidences, and vice versa. Experimental results are encouraging because they show that the proposed method can be a practical high-precision approach for extracting hyponyms for a given set of concepts.

One major problem in multilingual Question Answering (QA) is the combination of answers obtained from different languages into one single ranked list. This paper proposes a new method for tackling this problem. This method is founded on a graph-based ranking approach inspired in the popular Google's PageRank algorithm. Experimental results demonstrate that the proposed method outperforms other current techniques for answer fusion, and also evidence the advantages of multilingual QA over the traditional monolingual approach.

A major difficulty of supervised approaches for text classification is that they require a great number of training instances in order to construct an accurate classifier. This paper proposes a semi-supervised method that is specially suited to work with very few training examples. It considers the automatic extraction of unlabeled examples from the Web as well as an iterative integration of unlabeled examples into the training process. Preliminary results indicate that our proposal can significantly improve the classification accuracy in scenarios where there are less than ten training examples available per class.

This paper describes a QA system centered in a full data-driven architecture. It applies machine learning and text mining techniques to identify the most probable answers to factoid and definition questions respectively. Its major quality is that it mainly relies on the use of lexical information and avoids applying any complex language processing resources such as named entity classifiers, parsers and ontologies. Experimental results on the Spanish Question Answering task at CLEF 2006 show that the proposed architecture can be a practical solution for monolingual question answering by reaching a precision as high as 51%.

This paper describes the experiments performed for the QA@CLEF- 2006 within the joint participation of the eLing Division at VEng and the Language Technologies Laboratory at INAOE. The aim of these experiments was to observe and quantify the improvements in the final step of the Question Answering prototype when some syntactic features were included into the decision process. In order to reach this goal, a shallow approach to answer ranking based on the term density measure has been integrated into the weighting schema. This approach has shown an interesting improvement against the same prototype without this module. The paper discusses the results achieved, the conclusions and further directions within this research.

This work is a revised version of the paper “INAOE-UPV Joint Participation at CLEF 2005: Experiments in Monolingual Question Answering”, previously published in the CLEF 2005 working notes (www.clef-campaign.org/2005/working_notes/). This paper describes a full data-driven system for question answering. The system uses pattern matching and statistical techniques to identify the relevant passages as well as the candidate answers for factoid and definition questions. Since it does not consider any sophisticated linguistic analysis of questions and answers, it can be applied to different languages without requiring major adaptation changes. Experimental results on Spanish, Italian and French demonstrate that the proposed approach can be a convenient strategy for monolingual and multilingual question answering. CONACYT (Project Grant 43990); R2D2 (CICYTTIC2003-07158-C04-03); ICT EU-India (ALA/95/23/2003/077-054)

Traditional approaches for extractive summarization score/classify sentences based on features such as position in the text, word frequency and cue phrases These features tend to produce satisfactory summaries, but have the inconvenience of being domain dependent In this paper, we propose to tackle this problem representing the sentences by word sequences (n-grams), a widely used representation in text categorization The experiments demonstrated that this simple representation not only diminishes the domain and language dependency but also enhances the summarization performance.

Authorship attribution is the task of identifying the author of a given text. The main concern of this task is to define an appropriate characterization of documents that captures the writing style of authors. This paper proposes a new method for authorship attribution supported on the idea that a proper identification of authors must consider both stylistic and topic features of texts. This method characterizes documents by a set of word sequences that combine functional and content words. The experimental results on poem classification demonstrated that this method outperforms most current state-of-the-art approaches, and that it is appropriate to handle the attribution of short documents.

This paper describes the prototype developed in the Language Technologies Laboratory at INAOE for the Spanish monolingual QA evaluation task at CLEF 2005. The proposed approach copes with the QA task according to the type of question to solve (factoid or definition). In order to identify possible answers to factoid questions, the system applies a methodology centered in the use of lexical features. On the other hand, the system is supported by a pattern recognition method in order to identify answers to definition questions. The paper shows the methods applied at different stages of the system, with special emphasis on those used for answering factoid questions. Then the results achieved with this approach are discussed.

This paper describes a method for definition question answering based on the use of surface text patterns. The method is specially suited to answer questions about person’s positions and acronym’s descriptions. It considers two main steps. First, it applies a sequence-mining algorithm to discover a set of definition-related text patterns from the Web. Then, using these patterns, it extracts a collection of concept-description pairs from a target document database, and applies the sequence-mining algorithm to determine the most adequate answer to a given question. Experimental results on the Spanish CLEF 2005 data set indicate that this method can be a practical solution for answering this kind of definition questions, reaching a precision as high as 84%.

This paper presents a method for cross-language question answering. The method combines multiple query translations in order to improve the answering precision. The combination of translations is based on their pertinence to the target document collection rather than on their grammatical correctness. The pertinence is measured by the translation perplexity with respect to the collection language model. Experimental evaluation on question answering demonstrates that the proposed approach outperforms the results obtained by the best translation machine.

Spoken language identification consists in recognizing a language based on a sample of speech from an unknown speaker. The traditional approach for this task mainly considers the phonothactic information of languages. However, for marginalized languages –languages with few speakers or oral languages without a fixed writing standard–, this information is practically not at hand and consequently the usual approach is not applicable. In this paper, we present a method that only considers the acoustic features of the speech signal and does not use any kind of linguistic information. The experimental results on a pairwise discrimination task among nine languages demonstrated that our proposal is comparable to other similar methods. Nevertheless, its great advantage is the straightforward characterization of the acoustic signal.

Passage Retrieval (PR) is typically used as the first step in current Question Answering (QA) systems. Most methods are based on the vector space model allowing the finding of relevant passages for general user needs, but failing on selecting pertinent passages for specific user questions. This paper describes a simple PR method specially suited for the QA task. This method considers the structure of the question, favoring the passages that contain the longer n-gram structures from the question. Experimental results of this method on Spanish, French and Italian show that this approach can be useful for multilingual question answering systems.

The problem of the resolution of the lexical ambiguity seems to be stuck because of the knowledge acquisition bottleneck. Therefore, it is worthwhile to investigate the possibility of using the Web as a lexical resource. This paper explores two attempts of using Web counts collected through a search engine. The first approach calculates the hits of each possible synonym of the noun to disambiguate together with the nouns of the context. In the second approach the disambiguation of a noun uses a modifier adjective as supporting evidence. A better precision than the baseline was obtained using adjective-noun pairs, even if with a low recall. A comprehensive set of weighting formulae for combining Web counts was investigated in order to give a complete picture of what are the various possibilities, and what are the formulae that work best. The comparison across different search engines was also useful: Web counts, and consequently disambiguation results, were almost identical. Moreover, the Web seems to be more effective than the WordNet Domains lexical resource if integrated rather than stand-alone.

This paper discuses preliminary results on acoustic models creation through acoustic models already in existence for another language. In this work we show as case of study, the creation of acoustic models for Mexican Spanish, tagging automatically the training corpus with a recognition system for French. The resulting set of acoustic models for Mexican Spanish has gathered promising results at the phonetic level, reaching a recognition rate of 71.81%.

This paper explores the feasibility of a multilingual question answering approach based on the Web redundancy. The paper introduces a system prototype that combines a translation machine with a statistical QA method. The main advantage of this proposal is its small dependence to a given language. The experimental results, obtained from a test set of 165 factual questions, demostrated the great potential of the approach, and gave interesting insights about the redundancy of the web and the online translators.

This paper studies performance of various classifiers for Word Sense Disambiguation considering different training conditions. Our preliminary results indicate that the number and distribution of training examples has a great impact on the resulting precision. The Naive Bayes method emerged as the most adequate classifier for disambiguating words having few examples.

Nowadays there is a large amount of digital texts available for every purpose. New flexible and robust approaches are necessary for their access and analysis. This paper proposes a text exploration scheme based on hypertext, which incorporates some elements from information retrieval and text mining in order to transform the blind navigation of the hypertext into a step-by-step informed exploration. The proposed scheme is of relevance since it integrates three basic exploration functionalities, i.e. access, navigation and analysis. The paper also presents some preliminary results on the generation of hypertext from two text collections in an implementation of the scheme.

One major goal of human computer interfaces is to simplify the communication task. Traditionally, users have been restricted to the language of computers for this task. With the emerging of the graphical and multimodal interfaces the effort required for working with a computer is decreasing. However, the problem of communication is still present, and users continue caring about the communication task when they deal with a computer. Our work focuses on improving the communication between the human and the computer. This paper presents the foundations of a multimodal dialog model based on a modal logic, which integrates the speech and the action under the same framework.

In this paper a general description and current state of the project Dialogos Multimodales Inteligentes en Espanol (DIME) -Intelligent Multimodal Dialogs in Spanish- is presented. The purpose of the project is to develop a multimodal conversational agent with spoken input and output facilities in Spanish in a design oriented domain: kitchen design. In this paper, the state of the project, current results, an overview of the prototype system and future work are presented.