Genre-sensitive Neural Situation Entity classifier (DE, EN)
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. The advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable.We provide code for the basic local model (GRU), the local model with attention (GRU+attention), and our best performing context model which uses labels of previous clauses and genre information (GRU+attention+label+genre).The data w' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["classification","English","entity  types","German","situation entity"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. The advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable.We provide code for the basic local model (GRU), the local model with attention (GRU+attention), and our best performing context model which uses labels of previous clauses and genre information (GRU+attention+label+genre).The data w' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. The advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable.We provide code for the basic local model (GRU), the local model with attention (GRU+attention), and our best performing context model which uses labels of previous clauses and genre information (GRU+attention+label+genre).The data w
classification; English; entity  types; German; situation entity