A segmentation-free approach to OCR is presented as part of a knowledge-based word interpretation model. This new method is based on the recognition of subgraphs homeomorphic to previously defined prototypes of characters [16]. Gaps are identified as potential parts of characters by implementing a variant of the notion of relative neighborhood used in computational perception. In the system, ea...

This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The combines measurement methods: alignment-based and vector space-based. space-based method depends on semantic net that represents meaning of words as vectors. These vectors are lemmatized to enrich search space. generates an word space matrix (AWSM) texts according generated spaces....

distinguishing productive zones of a drilled oil well plays a very important role for petroleum engineers to decide where to perforate to produce oil. conventionally, net pay zones are determined by applying a set of cut-offs on perophysical logs. as a result, the conventional method finds productive intervals crisply. in this investigation, a net index value is proposed, then; diffusivity equa...

Various puzzles, quizzes, games, humorous definitions, or mathematical that should engage the interest of readers.

We introduce three linguistically motivated structured regularizers based on parse trees, topics, and hierarchical word clusters for text categorization. These regularizers impose linguistic bias in feature weights, enabling us to incorporate prior knowledge into conventional bagof-words models. We show that our structured regularizers consistently improve classification accuracies compared to ...

This paper presents NTT-NAIST SMT systems for EnglishGerman and German-English MT tasks of the IWSLT 2013 evaluation campaign. The systems are based on generalized minimum Bayes risk system combination of three SMT systems: forest-to-string, hierarchical phrase-based, phrasebased with pre-ordering. Individual SMT systems include data selection for domain adaptation, rescoring using recurrent ne...

Statistical dependency parsers have quickly gained popularity in the last decade by providing a good trade-off between parsing accuracy and parsing speed. Such parsers usually rely on handcrafted symbolic features and linear discriminative classifiers to make attachment choices. Recent work replaces these with dense word embeddings and neural nets with great success for parsing English and Chin...