2 research outputs found
Char-RNN and Active Learning for Hashtag Segmentation
We explore the abilities of character recurrent neural network (char-RNN) for
hashtag segmentation. Our approach to the task is the following: we generate
synthetic training dataset according to frequent n-grams that satisfy
predefined morpho-syntactic patterns to avoid any manual annotation. The active
learning strategy limits the training dataset and selects informative training
subset. The approach does not require any language-specific settings and is
compared for two languages, which differ in inflection degree.Comment: to appear in Cicling201
What do we need to know about an unknown word when parsing German
We propose a new type of subword embedding designed to provide more information about unknown compounds, a major source for OOV words in German. We present an extrinsic evaluation where we use the compound embeddings as input to a neural dependency parser and compare the results to the ones obtained with other types of embeddings. Our evaluation shows that adding compound embeddings yields a significant improvement of 2% LAS over using word embeddings when no POS information is available. When adding POS embeddings to the input, however, the effect levels out. This suggests that it is not the missing information about the semantics of the unknown words that causes problems for parsing German, but the lack of morphological information for unknown words. To augment our evaluation, we also test the new embeddings in a language modelling task that requires both syntactic and semantic information