Monotonicity Marking from Universal Dependency Trees
This work addresses a gap in connecting dependency parsing to monotonicity for NLP and computational linguistics, but it appears incremental as it builds on existing systems and uses a small dataset.
The paper tackled the problem of automatically annotating monotonicity information from Universal Dependency parse trees, a task with limited prior work, and reported that their system outperformed existing systems like NatLog and ccg2mono on a small evaluation dataset.
Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics. However, there is hardly any work that connects dependency parsing to monotonicity, which is an essential part of logic and linguistic semantics. In this paper, we present a system that automatically annotates monotonicity information based on Universal Dependency parse trees. Our system utilizes surface-level monotonicity facts about quantifiers, lexical items, and token-level polarity information. We compared our system's performance with existing systems in the literature, including NatLog and ccg2mono, on a small evaluation dataset. Results show that our system outperforms NatLog and ccg2mono.