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Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns
Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences.
ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction
ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes errors in high-level similarity harshly, and thus enable the regression model to obtain better prediction results for high similarity values.
ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context
Semantic relationships, such as hyponym-hypernym, cause-effect, meronym-holonym etc.
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