Quantum Models for Word- Sense Disambiguation
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2021
Författare
Hoffmann, Thomas
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In recent years, developments in machine learning had a tremendous impact on Natural
Language Processing (NLP). However, state-of-the-art language models contain
billions of parameters that require vast computational resources for optimization and
capture syntactic rules only from data, which does not allow an extensive analysis
of the underlying logic of language. Hence, to reduce the parameter space of NLP
models and close the gap between logic-based language models and statistical vector
space models, Coecke, Sadrzadeh, and Clark [11] introduce a compound framework
called Compositional Distributional Model of Meaning, based on Lambeks Pregroup
grammar and Quantum Theory.
This thesis investigates applying the Compositional Distributional Model of Meaning
on the word-sense disambiguation task by Kartsaklis, Sadrzadeh, and Pulman
[18]. Different quantum embeddings are evaluated in terms of disambiguation power,
given a matching context. One focus lies on the description of ambiguous words as
mixed states. Mixed states are probabilistic quantum states expressed as density
matrices which entail a lack of knowledge about the underlying system. Empirical
data was gathered from experiments using quantum circuits and classical computations.
We evaluate the performance and discuss the challenges and limitations of the
current quantum computing models. The results confirm the comprehensiveness of
the Compositional Distributional Model of Meaning and show statistical indications
for a richer representation of words by density matrices.
Beskrivning
Ämne/nyckelord
Quantum Natural Language Processing (QNLP) , Compositional Distributional Model of Meaning , Word-Sense Disambiguation , Quantum Computing