Semantic Analysis Using Probabilistic Topic Models And Markov Chains

David Zachary Hafner, Alejandro Molina Villegas, Edwyn Javier Aldana Bobadilla, Melesio Crespo Sánchez

Resultado de la investigación

Resumen

Probabilistic topic models are based upon the idea that documents are mixtures of topics, where a topic is a probability distribution over words. One important feature of those models is the assumption that the only observable variable is the number of times words are produced and their co-occurrence with other words. Representing the content of words and documents with probabilistic topics has one distinct advantage over a purely spatial representation like older approaches like LSA in probabilistic topic modeling. Each topic is individually interpretable, providing a probability distribution over words that picks out a coherent semantic cluster. In this research, we propose to process a set of de-identified transcripts of subjects using a probabilistic topic models technique known as Latent Dirichlet Allocation to create individual semantic models of mental states. Eventually, the modeled mental estates will be derived using Markov Chain Models. Until now, we have created a software project called “Psymantics” capable to apply a full processing to obtain topic models from a set of de-identified transcripts of subjects.
Idioma originalEnglish
EstadoPublished - 25 oct 2019
EventoAssociation for the Psychoanalysis of Culture & Society 2019 Annual Conference: Displacement: Precarity & Community - Rutgers University Inn and Conference Center, New Brunswick
Duración: 25 oct 201927 oct 2019
https://www.apcsweb.net/annual-conference/

Conference

ConferenceAssociation for the Psychoanalysis of Culture & Society 2019 Annual Conference
Título abreviadoAPCS 2019
PaísUnited States
CiudadNew Brunswick
Período25/10/1927/10/19
Dirección de internet

Huella dactilar

Markov processes
Semantics
Probability distributions
Statistical Models
Processing

Citar esto

Hafner, D. Z., Molina Villegas, A., Aldana Bobadilla, E. J., & Crespo Sánchez, M. (2019). Semantic Analysis Using Probabilistic Topic Models And Markov Chains. Papel presentado en Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, .
Hafner, David Zachary ; Molina Villegas, Alejandro ; Aldana Bobadilla, Edwyn Javier ; Crespo Sánchez, Melesio. / Semantic Analysis Using Probabilistic Topic Models And Markov Chains. Papel presentado en Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, .
@conference{caedc7e9d426475f9be75bb6f321461c,
title = "Semantic Analysis Using Probabilistic Topic Models And Markov Chains",
abstract = "Probabilistic topic models are based upon the idea that documents are mixtures of topics, where a topic is a probability distribution over words. One important feature of those models is the assumption that the only observable variable is the number of times words are produced and their co-occurrence with other words. Representing the content of words and documents with probabilistic topics has one distinct advantage over a purely spatial representation like older approaches like LSA in probabilistic topic modeling. Each topic is individually interpretable, providing a probability distribution over words that picks out a coherent semantic cluster. In this research, we propose to process a set of de-identified transcripts of subjects using a probabilistic topic models technique known as Latent Dirichlet Allocation to create individual semantic models of mental states. Eventually, the modeled mental estates will be derived using Markov Chain Models. Until now, we have created a software project called “Psymantics” capable to apply a full processing to obtain topic models from a set of de-identified transcripts of subjects.",
author = "Hafner, {David Zachary} and {Molina Villegas}, Alejandro and {Aldana Bobadilla}, {Edwyn Javier} and {Crespo S{\'a}nchez}, Melesio",
year = "2019",
month = "10",
day = "25",
language = "English",
note = "Association for the Psychoanalysis of Culture & Society 2019 Annual Conference : Displacement: Precarity & Community, APCS 2019 ; Conference date: 25-10-2019 Through 27-10-2019",
url = "https://www.apcsweb.net/annual-conference/",

}

Hafner, DZ, Molina Villegas, A, Aldana Bobadilla, EJ & Crespo Sánchez, M 2019, 'Semantic Analysis Using Probabilistic Topic Models And Markov Chains' Papel presentado en, New Brunswick, 25/10/19 - 27/10/19, .

Semantic Analysis Using Probabilistic Topic Models And Markov Chains. / Hafner, David Zachary; Molina Villegas, Alejandro; Aldana Bobadilla, Edwyn Javier; Crespo Sánchez, Melesio.

2019. Papel presentado en Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, .

Resultado de la investigación

TY - CONF

T1 - Semantic Analysis Using Probabilistic Topic Models And Markov Chains

AU - Hafner, David Zachary

AU - Molina Villegas, Alejandro

AU - Aldana Bobadilla, Edwyn Javier

AU - Crespo Sánchez, Melesio

PY - 2019/10/25

Y1 - 2019/10/25

N2 - Probabilistic topic models are based upon the idea that documents are mixtures of topics, where a topic is a probability distribution over words. One important feature of those models is the assumption that the only observable variable is the number of times words are produced and their co-occurrence with other words. Representing the content of words and documents with probabilistic topics has one distinct advantage over a purely spatial representation like older approaches like LSA in probabilistic topic modeling. Each topic is individually interpretable, providing a probability distribution over words that picks out a coherent semantic cluster. In this research, we propose to process a set of de-identified transcripts of subjects using a probabilistic topic models technique known as Latent Dirichlet Allocation to create individual semantic models of mental states. Eventually, the modeled mental estates will be derived using Markov Chain Models. Until now, we have created a software project called “Psymantics” capable to apply a full processing to obtain topic models from a set of de-identified transcripts of subjects.

AB - Probabilistic topic models are based upon the idea that documents are mixtures of topics, where a topic is a probability distribution over words. One important feature of those models is the assumption that the only observable variable is the number of times words are produced and their co-occurrence with other words. Representing the content of words and documents with probabilistic topics has one distinct advantage over a purely spatial representation like older approaches like LSA in probabilistic topic modeling. Each topic is individually interpretable, providing a probability distribution over words that picks out a coherent semantic cluster. In this research, we propose to process a set of de-identified transcripts of subjects using a probabilistic topic models technique known as Latent Dirichlet Allocation to create individual semantic models of mental states. Eventually, the modeled mental estates will be derived using Markov Chain Models. Until now, we have created a software project called “Psymantics” capable to apply a full processing to obtain topic models from a set of de-identified transcripts of subjects.

M3 - Paper

ER -

Hafner DZ, Molina Villegas A, Aldana Bobadilla EJ, Crespo Sánchez M. Semantic Analysis Using Probabilistic Topic Models And Markov Chains. 2019. Papel presentado en Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, .