Knowledge-Based News Event Analysis Toolkit
Oktie Hassanzadeh, Parul Awasthy, et al.
ISWC 2022
Quantum physics and mechanics have demonstrated significant advances and promising results in different areas using the current near-term devices. One emerging subarea in quantum machine learning is quantum natural language processing, which combines quantum computing advantages and speedups with language processing algorithms to create and perform natural language tasks such as text classification or generation. The libraries and toolboxes used in this subarea include DisCoPy and lambeq, which are used to transform sentences into string diagrams or monoidal functors, convert these diagrams into quantum circuits or ansatz and embed it into a quantum model. In this study, we used both libraries with different text-based datasets to perform sentiment analysis via classification. To do so, we create synthetic datasets to train the different models. After we obtain satisfactory results, we test the resulting models with known datasets. Despite its promising results, quantum natural language processing is far from achieving its full potential. To achieve this potential, the quantum software and hardware must be improved to make them suitable for use with more extensive and complex datasets and other tasks.
Oktie Hassanzadeh, Parul Awasthy, et al.
ISWC 2022
Samuel Thomas, Brian Kingsbury, et al.
ICASSP 2022
Mihaela Bornea, Lin Pan, et al.
AAAI 2021
Pierre Dognin, Inkit Padhi, et al.
EMNLP 2021