Implementation of interpretable methods for paraphrasing and text disambiguation

Visa enkel post
dc.contributor.authorCarlström, Klara
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.contributor.examinerWahde , Mattias
dc.contributor.supervisorWahde, Mattias
dc.date.accessioned2023-03-02T18:44:39Z
dc.date.available2023-03-02T18:44:39Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractIn this project, starting from an interpretable language model based on knowledge graphs, four essential methods for natural language processing (NLP) have been developed, namely (i) paraphrasing, (ii) part-of-speech tagging, (iii) semantic similarity analysis, and (iv) text simplification. The methods yield good results on a small dataset and thus offer promising prospects for continuing research on interpretable NLP. Applications of NLP are becoming increasingly embedded in our daily lives in applications such as voice assistants, automatic language translation, opinion mining and medical diagnostics. One of the reasons behind the exponentially growing interest in NLP is the development of deep neural network (DNN) models that have achieved outstanding performance on various NLP tasks. However, the domination of DNN models has been followed by deep concerns regarding the black-box nature of such systems. By contrast, the language model used here is fully interpretable, paving the way for safe and accountable NLP.
dc.identifier.coursecodeMMSX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/305999
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectnatural language processing
dc.subjectconversational AI
dc.subjectinterpretable AI
dc.subjectparaphrasing
dc.subjecttext disambiguation
dc.subjectknowledge graphs
dc.titleImplementation of interpretable methods for paraphrasing and text disambiguation
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
2023 Klara Carlström.pdf
Storlek:
2.89 MB
Format:
Adobe Portable Document Format
Beskrivning:
Ladda ner
License bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
license.txt
Storlek:
2.35 KB
Format:
Item-specific license agreed upon to submission
Beskrivning:
Ladda ner
Samling
Examensarbeten för masterexamen