🤖 AI Summary
This work addresses the three-class sentiment analysis task for financial texts by proposing QDisCoCirc, a hybrid architecture integrating quantum semantics with classical structural modeling. Methodologically, sentences are segmented and mapped onto shallow quantum circuits, where Bloch vector encoding yields interpretable quantum semantic representations; Combinatory Categorial Grammar (CCG) type embeddings explicitly encode syntactic structure, while a lightweight Transformer encoder captures word order and long-range dependencies. The key contribution is the first application of the QDisCoCirc framework to financial NLP—enabling synergistic syntax–semantics modeling without sacrificing quantum representation interpretability. Experiments demonstrate statistically significant improvements in macro-F1 on the test set. Ablation and attribution analyses confirm that predictions are predominantly driven by critical semantic blocks, and CCG type embeddings exhibit higher consistency in correctly classified instances, validating their role in enhancing structural coherence.
📝 Abstract
We apply quantum distributional compositional circuit (QDisCoCirc) to 3-class sentiment analysis of financial text. In our classical simulations, we keep the Hilbert-space dimension manageable by decomposing each sentence into short contiguous chunks. Each chunk is mapped to a shallow quantum circuit, and the resulting Bloch vectors are used as a sequence of quantum tokens. Simple averaging of chunk vectors ignores word order and syntactic roles. We therefore add a small Transformer encoder over the raw Bloch-vector sequence and attach a CCG-based type embedding to each chunk. This hybrid design preserves physically interpretable semantic axes of quantum tokens while allowing the classical side to model word order and long-range dependencies. The sequence model improves test macro-F1 over the averaging baseline and chunk-level attribution further shows that evidential mass concentrates on a small number of chunks, that type embeddings are used more reliably for correctly predicted sentences. For real-world quantum language processing applications in finance, future key challenges include circuit designs that avoid chunking and the design of inter-chunk fusion layers.