Concept Vocabularies Research Articles

Sometimes larger, sometimes smaller: Measuring vocabulary in monolingual and bilingual infants and toddlers.

Vocabulary size is a crucial early indicator of language development, for both monolingual and bilingual children. Assessing vocabulary in bilingual children is complex because they learn words in two languages, and there remains significant controversy about how to best measure their vocabulary size, especially in relation to monolinguals. This study compared monolingual vocabulary with different metrics of bilingual vocabulary, including combining vocabulary across languages to count either the number of words or the number of concepts lexicalized and assessing vocabulary in a single language. Data were collected from parents of 743 infants and toddlers aged 8-33 months learning French and/or English, using the MacArthur-Bates Communicative Development Inventories. The results showed that the nature and magnitude of monolingual-bilingual differences depended on how bilinguals' vocabulary was measured. Compared with monolinguals, bilinguals had larger expressive and receptive word vocabularies, similarly sized receptive concept vocabularies and smaller expressive concept vocabularies. Bilinguals' single-language vocabularies were smaller than monolinguals' vocabularies. The study highlights the need to better understand the role of translation equivalents in bilingual vocabulary development and the potential developmental differences in receptive and expressive vocabularies.

Recommendations for recognizing video events by concept vocabularies

Representing videos using vocabularies composed of concept detectors appears promising for generic event recognition. While many have recently shown the benefits of concept vocabularies for recognition, studying the characteristics of a universal concept vocabulary suited for representing events is ignored. In this paper, we study how to create an effective vocabulary for arbitrary-event recognition in web video. We consider five research questions related to the number, the type, the specificity, the quality and the normalization of the detectors in concept vocabularies. A rigorous experimental protocol using a pool of 1346 concept detectors trained on publicly available annotations, two large arbitrary web video datasets and a common event recognition pipeline allow us to analyze the performance of various concept vocabulary definitions. From the analysis we arrive at the recommendation that for effective event recognition the concept vocabulary should (i) contain more than 200 concepts, (ii) be diverse by covering object, action, scene, people, animal and attribute concepts, (iii) include both general and specific concepts, (iv) increase the number of concepts rather than improve the quality of the individual detectors, and (v) contain detectors that are appropriately normalized. We consider the recommendations for recognizing video events by concept vocabularies the most important contribution of the paper, as they provide guidelines for future work.

A query expansion framework in image retrieval domain based on local and global analysis

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall.

Proof of concept

In this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast growing interest in biomedical research, reflected by an exponential growth in scientific literature. An important problem for biomedical IR is dealing with the complex and inconsistent terminology encountered in biomedical publications. Dealing with the terminology problem requires domain knowledge stored in terminological resources: controlled indexing vocabularies and thesauri. The integration of this knowledge is, however, far from trivial. The first research theme investigates heuristics for obtaining word-based representations from biomedical text for robust retrieval. We investigated the effect of choices in document preprocessing heuristics on retrieval effectiveness. Document preprocessing heuristics such as stop word removal, stemming, and breakpoint identification and normalization were shown to strongly affect retrieval performance. An effective combination of heuristics was identified to obtain a word-based representation from text for the remainder of this thesis. The second research theme deals with concept-based retrieval. We compared a word-based to a concept-based representation and determined to what extent a manual concept-based representation can be automatically obtained from text. Retrieval based on only concepts was demonstrated to be significantly less effective than word-based retrieval. This deteriorated performance could be explained by errors in the classification process, limitations of the concept vocabularies and limited exhaustiveness of the concept-based document representations. Retrieval based on a combination of word-based and automatically obtained concept-based query representations did significantly improve word-only retrieval. In the third and last research theme we propose a cross-lingual framework for monolingual biomedical IR. In this framework, the integration of a concept-based representation is viewed as a cross-lingual matching problem involving a word-based and concept-based representation language. This framework gives us the opportunity to adopt a large set of established crosslingual information retrieval methods and techniques for this domain. Experiments with basic term-to-term translation models demonstrate that this approach can significantly improve word-based retrieval. Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based retrieval outside the biomedical domain. Available online from http://purl.utwente.nl/publications/72481.