1,664 research outputs found
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
Efficient Multi-Template Learning for Structured Prediction
Conditional random field (CRF) and Structural Support Vector Machine
(Structural SVM) are two state-of-the-art methods for structured prediction
which captures the interdependencies among output variables. The success of
these methods is attributed to the fact that their discriminative models are
able to account for overlapping features on the whole input observations. These
features are usually generated by applying a given set of templates on labeled
data, but improper templates may lead to degraded performance. To alleviate
this issue, in this paper, we propose a novel multiple template learning
paradigm to learn structured prediction and the importance of each template
simultaneously, so that hundreds of arbitrary templates could be added into the
learning model without caution. This paradigm can be formulated as a special
multiple kernel learning problem with exponential number of constraints. Then
we introduce an efficient cutting plane algorithm to solve this problem in the
primal, and its convergence is presented. We also evaluate the proposed
learning paradigm on two widely-studied structured prediction tasks,
\emph{i.e.} sequence labeling and dependency parsing. Extensive experimental
results show that the proposed method outperforms CRFs and Structural SVMs due
to exploiting the importance of each template. Our complexity analysis and
empirical results also show that our proposed method is more efficient than
OnlineMKL on very sparse and high-dimensional data. We further extend this
paradigm for structured prediction using generalized -block norm
regularization with , and experiments show competitive performances when
Speaker Dependent Voice Recognition with Word-Tense Association and Part-of-Speech Tagging
Extensive Research has been conducted on speech recognition and Speaker Recognition over the past few decades. Speaker recognition deals with identifying the speaker from multiple speakers and the ability to filter out the voice of an individual from the background for computational understanding. The more commonly researched method, speech recognition, deals only with computational linguistics. This thesis deals with speaker recognition and natural language processing. The most common speaker recognition systems are Text-Dependent and identify the speaker after a key word/phrase is uttered. This thesis presents Text-Independent Speaker recognition systems that incorporate the collaborative effort and research of noise-filtering, Speech Segmentation, Feature extraction, speaker verification and finally, Partial Language Modelling. The filtering process was accomplished using 4th order Butterworth Band-pass filters to dampen ambient noise outside normal speech frequencies of 300Hzto3000Hz. Speech segmentation utilizes Hamming windows to segment the speech, after which speech detection occurs by calculating the Short time Energy and Zero-crossing rates over a particular time period and identifying voiced from unvoiced using a threshold. Audio data collected from different people is run consecutively through a Speaker Training and Recognition Algorithm which uses neural networks to create a training group and target group for the recognition process. The output of the segmentation module is then processed by the neural network to recognize the speaker. Though not implemented here due to database and computational requirements, the last module suggests a new model for the Part of Speech tagging process that involves a combination of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) in a series configuration to achieve higher accuracy. This differs from existing research by diverging from the usual single model approach or the creation of hybrid ANN and HMM models
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