4,720 research outputs found
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
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Toward the automation of business process ontology generation
Semantic Business Process Management (SBPM) utilises semantic technologies (e.g., ontology) to model and query process representations. There are times in which such models must be reconstructed from existing textual documentation. In this scenario the automated generation of ontological models would be preferable, however current methods and technology are still not capable of automatically generating accurate semantic process models from textual descriptions. This research attempts to automate the process as much as possible by proposing a method that drives the transformation through the joint use of a foundational ontology and lexico-semantic analysis. The method is presented, demonstrated and evaluated. The original dataset represents 150 business activities related to the procurement processes of a case study company. As the evaluation shows, the proposed method can accurately map the linguistic patterns of the process descriptions to semantic patterns of the foundational ontology to a high level of accuracy, however further research is required in order to reduce the level of human intervention, expand the method so as to recognise further patterns of the foundational ontology and develop a tool to assist the business process modeller in the semi-automated generation of process models
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
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