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Punctuation performs an important role in language processing. However, automated speech recognization systems only output plain terms sequences. It really is then appealing to predict punctuations on simple word sequences. Earlier works are focused on using lexical features or prosodic cues captured from small corpus to predictable simple punctuations. When compared with simple punctuations, rich punctuation provides more meaningful information and are more challenging to predict. In this paper, LSDL model is suggested to predict rich punctuations on large-scale corpora. Experiments are performed on both in-domain and out-of-domain datasets for prediction of punctuations. The result of the Experiments shown that LSDL can significantly outperform the original CRF-based model. Furthermore, large-scale corpora are demonstrated to bring large improvement, and presenting POS tags and Chunking information in LSDL model on small corpus to improve performance.
LSDL, Deep Learning, Punctuation Prediction
Nowadays, with the fast development of IT, innumerable levels of information have been created and disseminated, a huge part which is speech information. The most frequent way to investigate speech data is to convert them into text message so that natural language processing techniques, such as analysis of sentiments, extraction of information and machine translation, can be applied. Research  has proven that punctuations are essential for these downstream processing. However outputs of the almost all of automated speech reorganization (ASR) systems  simply consist of streams of words.
There were some research on this problem, specifically, punctuation prediction or punctuation recovery. Most of earlier works rely on lexical features or prosodic cues . In such cases, supervised learning techniques are used, but there is no large and high-quality corpus for training such models, especially in Telugu language. Most of the research works focuses on small corpora such as PTB and CTB (Telugu Tree Bank), and manual
Our large-scale deep learning model achieves overwhelmingly improvement in rich punctuations prediction than traditional method. We are concluding that LSDL model has a better ability to predict the sentence boundary, but because of characteristics of Telugu language punctuation, there continues to be a issue of determining which punctuation to place at the boundary. Our work also shows that large-scale corpus helps promote the performance and generalization capability in both formal and informal corpora.
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