2 research outputs found
Bringing Semantic Structures to User Intent Detection in Online Medical Queries
The Internet has revolutionized healthcare by offering medical information
ubiquitously to patients via web search. The healthcare status, complex medical
information needs of patients are expressed diversely and implicitly in their
medical text queries. Aiming to better capture a focused picture of user's
medical-related information search and shed insights on their healthcare
information access strategies, it is challenging yet rewarding to detect
structured user intentions from their diversely expressed medical text queries.
We introduce a graph-based formulation to explore structured concept
transitions for effective user intent detection in medical queries, where each
node represents a medical concept mention and each directed edge indicates a
medical concept transition. A deep model based on multi-task learning is
introduced to extract structured semantic transitions from user queries, where
the model extracts word-level medical concept mentions as well as
sentence-level concept transitions collectively. A customized graph-based
mutual transfer loss function is designed to impose explicit constraints and
further exploit the contribution of mentioning a medical concept word to the
implication of a semantic transition. We observe an 8% relative improvement in
AUC and 23% relative reduction in coverage error by comparing the proposed
model with the best baseline model for the concept transition inference task on
real-world medical text queries.Comment: 10 pages, 2017 IEEE International Conference on Big Data (Big Data
2017
Joint Slot Filling and Intent Detection via Capsule Neural Networks
Being able to recognize words as slots and detect the intent of an utterance
has been a keen issue in natural language understanding. The existing works
either treat slot filling and intent detection separately in a pipeline manner,
or adopt joint models which sequentially label slots while summarizing the
utterance-level intent without explicitly preserving the hierarchical
relationship among words, slots, and intents. To exploit the semantic hierarchy
for effective modeling, we propose a capsule-based neural network model which
accomplishes slot filling and intent detection via a dynamic
routing-by-agreement schema. A re-routing schema is proposed to further
synergize the slot filling performance using the inferred intent
representation. Experiments on two real-world datasets show the effectiveness
of our model when compared with other alternative model architectures, as well
as existing natural language understanding services.Comment: In ACL 2019 as a long paper. Code and data available at
https://github.com/czhang99/Capsule-NL