This study looks at how well the leading monolingual English learners’ dictionaries in their online versions cope with misspelled words as search terms. Seven such dictionaries (Longman Dictionary of Contemporary English, free online version; Longman Dictionary of Contemporary English, premium subscription version; Merriam-Webster's English Learner's Online Dictionary; Macmillan English Diction...

ABSTARCT: The OpenMath language has been used to create mathematical objects in an XML-based language for establishing content dictionaries for various mathematical concepts. The content dictionaries are an excellent tool for both educational and research purposes. This research investigates the creation of content dictionaries for relational algebra, a theoretical language for describing opera...

Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are developed is also a challenge. In recent ye...

In this paper we investigatet the efficiency of the Orthogonal Matching Pursuit for random dictionaries. We concentrate on dictionaries satisfying Restricted Isometry Property. We introduce a stronger Homogenous Restricted Isometry Property which is satisfied with overwhelming probability for random dictionaries used in compressed sensing. We also present and discuss some open problems about OMP.

this article studies the generative grammar approach and some other theories driven from it to lexicon and lexical entries. generative grammar is known as a syntax-based theory, so some aspects of language which have been studied in this theory may have been ignored in other theories. this article shows that more attention has been given to lexicon and lexical entries since 50s. we may claim th...

In this paper we provide a quantitative evaluation of information automatically extracted from machine readable dictionaries. Our results show that for any one dictionary, 55-70% of the extracted information is garbled in some way. However, we show that these results can be dramatically reduced to about 6% by combining the information extracted from five dictionaries. It therefore appears that ...