3 research outputs found
Spanish named entity recognition in the biomedical domain
Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft
Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks
Prediction tasks about students have practical significance for both student
and college. Making multiple predictions about students is an important part of
a smart campus. For instance, predicting whether a student will fail to
graduate can alert the student affairs office to take predictive measures to
help the student improve his/her academic performance. With the development of
information technology in colleges, we can collect digital footprints which
encode heterogeneous behaviors continuously. In this paper, we focus on
modeling heterogeneous behaviors and making multiple predictions together,
since some prediction tasks are related and learning the model for a specific
task may have the data sparsity problem. To this end, we propose a variant of
LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the
student profile-aware representation from heterogeneous behavior sequences. The
proposed soft-attention mechanism can dynamically learn different importance
degrees of different days for every student. In this way, heterogeneous
behaviors can be well modeled. In order to model interactions among multiple
prediction tasks, we propose a co-attention mechanism based unit. With the help
of the stacked units, we can explicitly control the knowledge transfer among
multiple tasks. We design three motivating behavior prediction tasks based on a
real-world dataset collected from a college. Qualitative and quantitative
experiments on the three prediction tasks have demonstrated the effectiveness
of our model