Temporal Topic Embeddings with a Compass

Simple item page
dc.contributor.authorPalamarchuk, Daniel Andrewen
dc.contributor.committeechairNorth, Christopher L.en
dc.contributor.committeememberDanielson, Thomas Leeen
dc.contributor.committeememberMayer, Brian Benjaminen
dc.contributor.committeememberWang, Xuanen
dc.contributor.departmentComputer Science and#38; Applicationsen
dc.date.accessioned2024-05-23T08:00:35Zen
dc.date.available2024-05-23T08:00:35Zen
dc.date.issued2024-05-22en
dc.description.abstractAligning Word2vec word embeddings using a compass in a system of Compass-aligned Distributional Embeddings (CADE) creates stable and accurate temporal word embeddings. This thesis seeks to expand the CADE framework into the area of dynamic topic modeling (DTM), where temporal word2vec embeddings can be used to describe temporally and unsupervised evolving topics. It also seeks to improve upon the CADE framework through a theoretical and experimental exploration of compass parameters, cluster and topic generation techniques, and topic descriptor creation. This method of Temporal Topic Embeddings with a Compass (TTEC) will be compared to other DTM techniques in the ability to create coherent and diverse clusters and will be shown to be competitive compared to traditional and transformer-aided DTM architectures. In addition to a qualitative discussion of results, there will be a political theoretical overview of the nature of this technique and potential use cases, with interviews from political actors of various backgrounds as to how the technique and machine learning as a whole can be used in the organizational setting.en
dc.description.abstractgeneralDiachronic word embeddings look at how the context words appear in evolve over time. Dynamic Topic Modeling (DTM) is the ability to computationally discover topics and how they evolve over time. This thesis creates a DTM technique called Temporal Topic Embeddings with a Compass (TTEC) based off diachronic word embeddings, allowing a user to simultaneously look at word and topic evolution over time. There is also an exploration of the use case of TTEC and similar machine learning models within various political organizational settings through interviews.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:40595en
dc.identifier.urihttps://hdl.handle.net/10919/119057en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.subjectdiachronicen
dc.subjectneural networken
dc.subjecthcien
dc.subjectbureaucracyen
dc.subjectlegibilityen
dc.titleTemporal Topic Embeddings with a Compassen
dc.typeThesisen
thesis.degree.disciplineComputer Science & Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Palamarchuk_DA_T_2024.pdf
Size:
2.12 MB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses