3,148 research outputs found
A Sub-Character Architecture for Korean Language Processing
We introduce a novel sub-character architecture that exploits a unique
compositional structure of the Korean language. Our method decomposes each
character into a small set of primitive phonetic units called jamo letters from
which character- and word-level representations are induced. The jamo letters
divulge syntactic and semantic information that is difficult to access with
conventional character-level units. They greatly alleviate the data sparsity
problem, reducing the observation space to 1.6% of the original while
increasing accuracy in our experiments. We apply our architecture to dependency
parsing and achieve dramatic improvement over strong lexical baselines.Comment: EMNLP 201
Radical-Enhanced Chinese Character Embedding
We present a method to leverage radical for learning Chinese character
embedding. Radical is a semantic and phonetic component of Chinese character.
It plays an important role as characters with the same radical usually have
similar semantic meaning and grammatical usage. However, existing Chinese
processing algorithms typically regard word or character as the basic unit but
ignore the crucial radical information. In this paper, we fill this gap by
leveraging radical for learning continuous representation of Chinese character.
We develop a dedicated neural architecture to effectively learn character
embedding and apply it on Chinese character similarity judgement and Chinese
word segmentation. Experiment results show that our radical-enhanced method
outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure
Component-Enhanced Chinese Character Embeddings
Distributed word representations are very useful for capturing semantic
information and have been successfully applied in a variety of NLP tasks,
especially on English. In this work, we innovatively develop two
component-enhanced Chinese character embedding models and their bigram
extensions. Distinguished from English word embeddings, our models explore the
compositions of Chinese characters, which often serve as semantic indictors
inherently. The evaluations on both word similarity and text classification
demonstrate the effectiveness of our models.Comment: 6 pages, 2 figures, conference, EMNLP 201
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
We present a novel neural network model that learns POS tagging and
graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to
learn feature representations shared for both POS tagging and dependency
parsing tasks, thus handling the feature-engineering problem. Our extensive
experiments, on 19 languages from the Universal Dependencies project, show that
our model outperforms the state-of-the-art neural network-based
Stack-propagation model for joint POS tagging and transition-based dependency
parsing, resulting in a new state of the art. Our code is open-source and
available together with pre-trained models at:
https://github.com/datquocnguyen/jPTDPComment: v2: also include universal POS tagging, UAS and LAS accuracies w.r.t
gold-standard segmentation on Universal Dependencies 2.0 - CoNLL 2017 shared
task test data; in CoNLL 201
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