Search results for " natural language"
showing 10 items of 192 documents
"Table 55" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"
2019
Total selection efficiency (i.e., the fraction of MC HECOs surviving the trigger and offline selection criteria) as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 1000 GeV.
"Table 33" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"
2019
Total selection efficiency (i.e., the fraction of MC HECOs surviving the trigger and offline selection criteria) as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 2000 GeV.
"Table 43" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"
2019
Total selection efficiency (i.e., the fraction of MC HECOs surviving the trigger and offline selection criteria) as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 3000 GeV.
"Table 25" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"
2019
Total selection efficiency (i.e., the fraction of MC HECOs surviving the trigger and offline selection criteria) as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 2000 GeV.
"Table 49" of "Search for magnetic monopoles and stable high-electric-charge objects in 13 TeV proton-proton collisions with the ATLAS detector"
2019
Total selection efficiency (i.e., the fraction of MC HECOs surviving the trigger and offline selection criteria) as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 2000 GeV.
Discovering learning paths on a domain ontology using natural language interaction
2005
The present work investigates the problem of determining a learning path inside a suitable domain ontology. The proposed approach enables the user of a web learning application to interact with the system using natural language in order to browse the ontology itself. The course related knowledge is arranged as a three level hierarchy: content level, symbolic level, and conceptual level bridging the previous ones. The implementation of the ontological, the interaction, and the presentation component inside the TutorJ system is explained, and the first results are presented.
"Table 8" of "Measurement of charged-particle event shape variables in sqrt(s) = 7 TeV proton-proton interactions with the ATLAS detector"
2014
Mean Values of Thrust, Thrust Minor and Sphericity verses charged particle PT scalar sum.
"Table 4" of "Measurement of the differential cross sections for isolated direct photon pair production in $p \bar p$ collisions at $\sqrt{s} = 1.96$…
2013
The measured differential distribution in the polar scattering angle in the Collins-Soper frame;.
Extracting Semantic Knowledge from Unstructured Text Using Embedded Controlled Language
2016
Nowadays, most of the data on the Web is still in the form of unstructured text. Knowledge extraction from unstructured text is highly desirable but extremely challenging due to the inherent ambiguity of natural language. In this article, we present an architecture of an information extraction system based on the concept of Embedded Controlled Language that allows for extracting formal semantic knowledge from an unstructured text corpus. Moreover, the presented approach has a potential to support multilingual input and output.
Self-service Ad-hoc Querying Using Controlled Natural Language
2016
The ad-hoc querying process is slow and error prone due to inability of business experts of accessing data directly without involving IT experts. The problem lies in complexity of means used to query data. We propose a new natural language- and semistar ontology-based ad-hoc querying approach which lowers the steep learning curve required to be able to query data. The proposed approach would significantly shorten the time needed to master the ad-hoc querying and to gain the direct access to data by business experts, thus facilitating the decision making process in enterprises, government institutions and other organizations.