Co-occurrence

Summary

In linguistics, co-occurrence or cooccurrence is an above-chance frequency of occurrence of two terms (also known as coincidence or concurrence) from a text corpus alongside each other in a certain order. Co-occurrence in this linguistic sense can be interpreted as an indicator of semantic proximity or an idiomatic expression. Corpus linguistics and its statistic analyses reveal patterns of co-occurrences within a language and enable to work out typical collocations for its lexical items. A co-occurrence restriction is identified when linguistic elements never occur together. Analysis of these restrictions can lead to discoveries about the structure and development of a language. Co-occurrence can be seen an extension of word counting in higher dimensions. Co-occurrence can be quantitatively described using measures like correlation or mutual information. See also

Distributional hypothesis
Statistical semantics
Idiom (language structure)
Co-occurrence matrix
Co-oc

Official source

https://en.wikipedia.org/wiki/Co-occurrence

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We present two models for content-based automatic image annotation and retrieval in web image repositories, based on the co-occurrence of tags and visual features in the images. In particular, we show how additional measures can be taken to address the noisy and limited tagging problems, in datasets such as Flickr, to improve performance. As in many state-of-the-art works, an image is represented as a bag of visual terms computed using edge and color information. The cooccurrence information of visual terms and tags is used to create models for image annotation and retrieval. The first model begins with a naive Bayes approach and then improves upon it by using image pairs as single documents to significantly reduce the noise and increase annotation performance. The second method models the visual terms and tags as a graph, and uses query expansion techniques to improve the retrieval performance. We evaluate our methods on the commonly used 150 concept Corel dataset, and a much harder 2000 concept Flickr dataset.

Idiap2009

Sequence motifs are of greater biological importance in nucleotide and protein sequences. The conserved occurrence of identical motifs represents the functional significance and helps to classify the biological sequences. In this paper, a new algorithm is proposed to find all identical motifs in multiple nucleotide or protein sequences. The proposed algorithm uses the concept of dynamic programming. The application of this algorithm includes the identification of (a) conserved identical sequence motifs and (b) identical or direct repeat sequence motifs across multiple biological sequences (nucleotide or protein sequences). Further, the proposed algorithm facilitates the analysis of comparative internal sequence repeats for the evolutionary studies which helps to derive the phylogenetic relationships from the distribution of repeats. © 2010 Springer-Verlag.

Springer Berlin Heidelberg2010

Intégration de connaissances syntaxiques et sémantiques dans les représentations vectorielles de textes

Romaric Besançon

The notion of similarity between texts is fundamental for many applications of Natural Language Processing. For example, this notion is particularly useful for the applications designed for the management of information in large textual databases, such as Information Retrieval or Automatic Text Structuring. Information Retrieval is the search of the most relevant documents according to an information need expressed by a query, and can be implemented by the search of the documents most similar to the query. Automatic Text Structuring is often viewed as the clustering of documents according to their similarity measures. The similarity between documents relies on their representation. The most used textual representation is the Vector Space model, in which each document is represented by a vector, and the similarity between documents is then computed by a distance measure in this space, for instance, the cosine of the vectors representing the documents. We first present several vector space models used for the computation of similarities between documents. Then, we focus on the problem of the integration of additional knowledge in the vector space representation, and the impact of this integration on the results obtained for several tasks. We fist consider the integration of co-occurrences in the representation model, and we focus on the DSIR model (Distributional Semantics based In-formation Retrieval). We show that this model has a probabilistic theoretical basis. We then consider the use of syntactic information to compute the co-occurrence frequencies. We also consider the integration of knowledge about compounds in the representation, taking into account morpho-syntactic and semantic variants of the considered compounds. We finally address the issue of word sense disambiguation, using synonymy relations to derive a vector space representation for which each dimension is associated to a meaning and not to a term. For all these methods, we propose several evaluations : we first consider a validation for the notion of similarity derived from a vector space representation, in a multi-lingual framework : the idea is to verify that the similarity between two documents in one language is close to the similarity between their translations in another language. We also propose an evaluation of the different models considered in a standard Information Retrieval evaluation framework. We finally consider the evaluation of the models on a Word Sense Disambiguation task.

EPFL2002

Intégration de connaissances syntaxiques et sémantiques dans les représentations vectorielles de textes

Romaric Besançon

EPFL2002