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A Joint Model for Word Embedding and Word Morphology
This paper presents a joint model for
performing unsupervised morphological
analysis on words, and learning a
character-level composition function
from morphemes to word embeddings.
Our model splits individual words into
segments, and weights each segment
according to its ability to predict context
words. Our morphological analysis is
comparable to dedicated morphological
analyzers at the task of morpheme boundary
recovery, and also performs better
than word-based embedding models at
the task of syntactic analogy answering.
Finally, we show that incorporating
morphology explicitly into character-level
models helps them produce embeddings
for unseen words which correlate better
with human judgments
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