391 research outputs found
Evaluating automatic cross-domain Dutch semantic role annotation
In this paper we present the first corpus where one million Dutch words from a variety of text genres have been annotated with semantic roles. 500K have been completely manually verified and used as training material to automatically label another 500K. All data has been annotated following an adapted version of the PropBank guidelines. The corpus’s rich text type diversity and the availability of manually verified syntactic dependency structures allowed us to experiment with an existing semantic role labeler for Dutch. In order to test the system’s portability across various domains, we experimented with training on individual domains and compared this with training on multiple domains by adding more data. Our results show that training on large data sets is necessary but that including genre-specific training material is also crucial to optimize classification. We observed that a small amount of in-domain training data is already sufficient to improve our semantic role labeler
The Role of Semantic Parsing in Understanding Procedural Text
In this paper, we investigate whether symbolic semantic representations,
extracted from deep semantic parsers, can help reasoning over the states of
involved entities in a procedural text. We consider a deep semantic
parser~(TRIPS) and semantic role labeling as two sources of semantic parsing
knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural
reasoning framework. Second, we integrate semantic parsing information into
state-of-the-art neural models to conduct procedural reasoning. Our experiments
indicate that explicitly incorporating such semantic knowledge improves
procedural understanding. This paper presents new metrics for evaluating
procedural reasoning tasks that clarify the challenges and identify differences
among neural, symbolic, and integrated models.Comment: 9 pages, Appected in EACL202
Neural Combinatory Constituency Parsing
東京都立大学Tokyo Metropolitan University博士(情報科学)doctoral thesi
Abstractive Multi-Document Summarization based on Semantic Link Network
The key to realize advanced document summarization is semantic representation of documents. This paper investigates the role of Semantic Link Network in representing and understanding documents for multi-document summarization. It proposes a novel abstractive multi-document summarization framework by first transforming documents into a Semantic Link Network of concepts and events and then transforming the Semantic Link Network into the summary of the documents based on the selection of important concepts and events while keeping semantics coherence. Experiments on benchmark datasets show that the proposed summarization approach significantly outperforms relevant state-of-the-art baselines and the Semantic Link Network plays an important role in representing and understanding documents
- …