[PDF] Top 20 La atribucin intelectual en las revistas mdicas cubanas

... the bag of words representation (3) is categorical in essence rather than temporal since no time information is ...two document positions. The main idea behind the locally ... See full document

... of words in a continuous space, how could we best use it in IR/clustering tasks? Especially, could we develop probabilistic models which would be able to bene- fit from this a priori information on the similarity ... See full document

... a bag-of-words representation of the document repos- ...Highly weighted term-match features are then used to find a decod- ing path that gives highest score to the document that ... See full document

... are bag of words with dependency constraints which are automatically extracted from the Penn treebank using head percolation rules used in (Magerman, 1995), which do not contain any or- der ... See full document

... Users usually sketch with large variations therefore there is no absolute spatial relationship between different parts of an object. It is finding co-occurring words of two images. The goal of spatial constraint ... See full document

... In this analysis, we compare the word importance weights learned by the NBOW2 model with tf- idf weights and other word weight features pro- posed in the previous works. For this compar- ison, an SVM classifier is used ... See full document

... Although the BWs method has many advantages, it su ff ers from its lack of spatial information. Some methods focus on integrating the spatial layout information into the BWs method. Lazebnik et al. [20] proposes a ... See full document

... (or words, as here) in the query. Though in Figure 3 four key words are signified, one or two words are attended to in the first hop in the com- mon ...key words and generating a query-aware ... See full document

... We propose a method that regard both the refer- ence translation (appears in the training set) and the bag-of-words as the targets of Seq2Seq at the training stage. Experimental results show that our model ... See full document

... We experiment with knowledge-enhanced cloze- style reading comprehension using the Common Nouns and Named Entities partitions of the Chil- dren’s Book Test (CBT) dataset (Hill et al., 2015). In the CBT cloze-style task a ... See full document

... of words in the comments to be processed, the average number of words They have the comments and the maximum number of words that the comments ... See full document

... One interesting side result from this proof is an approximation to the general Green's function using the uniform Green's function and a rescaling of the independent variable that locall[r] ... See full document

... The document's content is represented by a list of words and phrases, and a each weighted to represent list quency of concepts from the its importance in characterizing the document, cat[r] ... See full document

... their meta-embeddings created using the proposed method (Meta) and concatenation baseline (CONC). From Figure 2, we see that the meta embeddings obtain the best perfor- mance across all overlap ratios. The improvements ... See full document

... Each article was split into sentences and for each of these sentences (containing at least 10 words) it was determined whether it is quoted in any of the comments as described above. In case the quoteScore was ... See full document

... To address this problem, this paper couples the color information into the BoF model as a complement to sift features, two different features are extracted to represent the same interest[r] ... See full document

... Trademarks can be categorized into a few different types. A trade- mark can be a word-only mark, a device-only mark or a device- and-word mark. For a word-only mark, the design of the trademark consists purely of text ... See full document

... utilizes bag of words model with Speeded Up Robust Features and demonstrate classification over 180 fundus images containing lesions (hard exudates, soft exudates, microaneu- rysms, and haemorrhages) and ... See full document

... dataset. We learn optimal k with respect to task, but not embedding space, due to significant den- sity of the semantic space of word embeddings, see Note on Clustering Word Embeddings. Baselines: We consider two ... See full document

... abbreviated words and sometimes we find that there are keystrokes errors, with a complexity of the corrected in an automatic way (possible that will be our objective in the next ... See full document