723 0 Bayesian parameter estimation Web Service SOAPSoaplab

Specified Name (from WSDL): AnalysisServiceService |

aka estimate_bayesian_parameters Add_annotation_inactive

Categories:

Lexicon/Terminology Extraction

Languages:

Language Independent

View the comments on this service

Overview

This looks like a Soaplab service. Click here for more info and guidance on how to use this service

Provider:
Universitat Pompeu Fabra (UPF)

Location:
SPAIN

Submitter / Source:
Marc Poch Riera (over 12 years ago)

Base URL:
http://ws03.iula.upf.edu/soaplab2-axis/services/lexicon_terminology_extraction.estimate_bayesian_parameters

WSDL Location:
http://ws03.iula.upf.edu/soaplab2-axis/services/lexicon_terminology_extraction.estimate_bayesian_parameters?wsdl (download last cached WSDL file)

Run it online!!

Documentation URL(s):

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Muntsa Padró (over 12 years ago)

http://ws02.iula.upf.edu/panacea/documentation/ws03/WS_estimate_bayesian_parameters.pdf

Description(s):

Muntsa Padró (over 12 years ago)

Given a training set encoded as vectors of cue (or feature) occurrences, this web service estimates the parameters P(cuei|class): the probability of seeing each cue as a member or non-member of the class. This estimation is performed using Bayesian inference, which combines prior knowledge with observed data. The parameters estimated with this web service can be used, for example, to classify new instances using a Naive Bayes
classifier. The output format is the one needed as input for the naive_bayes_classifier webservice.

Details (from Soaplab server):

from

header=[] body=[Provider's description doc] cssheader=[boxoverTooltipHeader] cssbody=[boxoverTooltipBody] delay=[200]

Soaplab server (over 10 years ago)

ds_lsr_analysis :

analysis :

type : Lexicon_Terminology_Extraction
analysis_extension :

name : estimate_bayesian_parameters
input :
description : cat: donat el conjunt de signatures d'entrenament en un fitxer weka (train_file.arff), estimar el pes ("theta") de cada pista usant les frequències de les dades i el coneixement a priori donat al fitxer de priors (priors.csv). Com a paràmetre d'entrada es pot posar la mida que donem a les dades virtuals.
es: dado el conjunto de signaturas de entrenamiento en un fichero weka (train_file.arff), estimar el peso ("theta") de cada pista usando las frecuencias de los datos y el conocimiento a priori dado en el fichero de priors (priors.csv). Cómo parámetro de entrada se puede poner la medida que damos a los datos virtuales (peso del prior).
en: given the set of training signatures in a weka file (train_file.arff), estimate the weight ("theta") of each cue using data frequencies and prior knowledge given in priors file (priors.csv). As a parameter, we can give the size to give to virtual size (weight of the priors).
installation : Soaplab2 default installation
output :

Show all

ds_lsr_analysis :

analysis :

type : Lexicon_Terminology_Extraction
analysis_extension :

option :
- type : normal
- name : emboss
- value : false
- type : normal
- name : installation
- value : Soaplab2 default installation
event :

id : _E_1
action :

app_info :

category : lexicon_terminology_extraction

parameter :
- base :
- data :
- base :
- data :
- base :
- range :
- base :
- range :
- base :
- data :

name : estimate_bayesian_parameters
input :
description : cat: donat el conjunt de signatures d'entrenament en un fitxer weka (train_file.arff), estimar el pes ("theta") de cada pista usant les frequències de les dades i el coneixement a priori donat al fitxer de priors (priors.csv). Com a paràmetre d'entrada es pot posar la mida que donem a les dades virtuals.
es: dado el conjunto de signaturas de entrenamiento en un fichero weka (train_file.arff), estimar el peso ("theta") de cada pista usando las frecuencias de los datos y el conocimiento a priori dado en el fichero de priors (priors.csv). Cómo parámetro de entrada se puede poner la medida que damos a los datos virtuales (peso del prior).
en: given the set of training signatures in a weka file (train_file.arff), estimate the weight ("theta") of each cue using data frequencies and prior knowledge given in priors file (priors.csv). As a parameter, we can give the size to give to virtual size (weight of the priors).
installation : Soaplab2 default installation
output :

License(s): Info

None

Cost:

Santi (over 11 years ago)

This Web Service is free for research use. It was developed in the framework of the PANACEA project, funded by the European Union Language Technologies Area, Information and Communication Technologies, of the 7th Framework Programme (7FP-ITC-248064).

Usage Conditions: Info

Santi (over 11 years ago)

These Terms and Conditions of Service will apply to your use of this web-service and by using the web-service, you are agreeing to these Terms and Conditions of Service.

(a) Authorized Users: users affiliated with a research or non-profit educational institution.

(b) Authorized Use: Restricted to use for research or educational purposes. You should have the right to legally use the “Input data” to the web service and hence acquire all rights to the output data.

(c) Prohibited use: commercial purposes, including charging a fee-for-service; it is also prohibited to use input data for which you did not obtain the necessary rights.

(d) Warranty; Disclaimers: authorized Users recognize that IULA is an aggregator of third-party web services. This web-service owner warrants that to its knowledge use of the web service in accordance with the terms of this document will not infringe the copyright of any third party.

(e) Liability: The web service and IULA will not be liable, and Users agree that they will not hold IULA or the web service liable for any loss, injury, claim, liability, damages, costs, and/or attorneys fees of any kind that result from the use of this web service.

Contact Info: Info

Santi (over 11 years ago)

iulatrl@upf.edu

Publications: Info

silvia.arano (over 11 years ago)

http://hdl.handle.net/10230/20329

Citations: Info

silvia.arano (over 11 years ago)

@inproceedings{padro_web_2012,
address = {Stroudsburg ({USA)}},
title = {Web Services for Bayesian Learning},
volume = {29-31},
url = {http://hdl.handle.net/10230/20329},
booktitle = {{EACL} 2012: Workshop on Computational Models of Language Acquisition and Loss: Proceedings of the Conference; 2012 April 24; Avignon, France},
publisher = {Association for Computational Linguistics ({ACL)}},
author = {Padró, Muntsa and Bel, Núria},
year = {2012}
}

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SOAP Operations (23)

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createAndRun

1 input | 1 output

Part of Service: Bayesian parameter estimation Web Service