Multilingual Modal Sense Classification using a Convolutional Neural Network [Source Code]
<|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:'AbstractModal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal&rsquo;s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We bench-mark the CNN on a standard WSD task,where it compares favorably to models using sense-disambiguated target vectors. (Marasović and Frank, 2016)</p' . 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" : ["Modal sense classification (MSC)","modal verb","semantic feature","word embedding","Word Sense Disambiguation"]} <|eot_id|>
 Here is the Description of a dataset. Description:'AbstractModal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal&rsquo;s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We bench-mark the CNN on a standard WSD task,where it compares favorably to models using sense-disambiguated target vectors. (Marasović and Frank, 2016)</p' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
AbstractModal sense classification (MSC) is aspecial WSD task that depends on themeaning of the proposition in the modal&rsquo;s scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We bench-mark the CNN on a standard WSD task,where it compares favorably to models using sense-disambiguated target vectors. (Marasović and Frank, 2016)</p
Modal sense classification (MSC); modal verb; semantic feature; word embedding; Word Sense Disambiguation