5,906 research outputs found
Dual Long Short-Term Memory Networks for Sub-Character Representation Learning
Characters have commonly been regarded as the minimal processing unit in
Natural Language Processing (NLP). But many non-latin languages have
hieroglyphic writing systems, involving a big alphabet with thousands or
millions of characters. Each character is composed of even smaller parts, which
are often ignored by the previous work. In this paper, we propose a novel
architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to
learn sub-character level representation and capture deeper level of semantic
meanings. To build a concrete study and substantiate the efficiency of our
neural architecture, we take Chinese Word Segmentation as a research case
example. Among those languages, Chinese is a typical case, for which every
character contains several components called radicals. Our networks employ a
shared radical level embedding to solve both Simplified and Traditional Chinese
Word Segmentation, without extra Traditional to Simplified Chinese conversion,
in such a highly end-to-end way the word segmentation can be significantly
simplified compared to the previous work. Radical level embeddings can also
capture deeper semantic meaning below character level and improve the system
performance of learning. By tying radical and character embeddings together,
the parameter count is reduced whereas semantic knowledge is shared and
transferred between two levels, boosting the performance largely. On 3 out of 4
Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to
0.4%. Our results are reproducible, source codes and corpora are available on
GitHub.Comment: Accepted & forthcoming at ITNG-201
Neural Word Segmentation with Rich Pretraining
Neural word segmentation research has benefited from large-scale raw texts by
leveraging them for pretraining character and word embeddings. On the other
hand, statistical segmentation research has exploited richer sources of
external information, such as punctuation, automatic segmentation and POS. We
investigate the effectiveness of a range of external training sources for
neural word segmentation by building a modular segmentation model, pretraining
the most important submodule using rich external sources. Results show that
such pretraining significantly improves the model, leading to accuracies
competitive to the best methods on six benchmarks.Comment: Accepted by ACL 201
Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context
Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection
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