Discovering whether words are semantically related and identifying the specific semantic relation that holds between them is of crucial importance for automatic reasoning on text data. For that purpose, different methodologies have been proposed that either (1) tackle feature engineering, (2) fine-tune latent semantic spaces, or (3) take advantage of cognitive links between semantic relations in multitask settings. In this paper, we investigate how feature engineering and multitask architectures can be improved and consequently combined to identify lexico-semantic relations. Evaluation results over a set of gold-standard datasets show that (1) combinations of similar features are beneficial (features sets), (2) asymmetric distributional features are a strong cue to discriminate asymmetric relations as well as they play an important role in multitask architectures, (3) shared-private models improve over binary and fully-shared classifiers as well as they correctly balance the focus on features between private and shared layers\footnote{Both the code and the datasets are available at https://anonymous for reproducibility.}.

Understanding Feature Focus in Multitask Settings for Lexico-semantic Relation Identification

Findings Papers - Semantics: Lexical Semantics

technical paper

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Understanding Feature Focus in Multitask Settings for Lexico-semantic Relation Identification

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