Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and is a fundamental task in discourse analysis. For Chinese, previous researches identify EDUs just through discriminating the functions of punctuations. In this paper, we argue that Chinese EDUs may not end at the punctuation positions and should follow the definition of EDU in RST-DT. With this definition, we conduct Chinese discourse segmentation with the help of English labeled data.Using discourse commonality between English and Chinese, we design an adversarial neural network framework to extract common language-independent features and language-specific features which are useful for discourse segmentation, when there is no or only a small scale of Chinese labeled data available. Experiments on discourse segmentation demonstrate that our models can leverage common features from bilingual data, and learn efficient Chinese-specific features from a small amount of Chinese labeled data, outperforming the baseline models.
Recently, much work have been conducted on English EDU segmentation. Most of those segmenters are based on statistical classifiers and leverage many manually extracted features in which lexical syntactic subtree features play an important role @cite_27 . Some modifications to the statistic model and features have made great improvements @cite_46 @cite_9 and @cite_44 achieved the best results ever. Besides, @cite_38 first used neural networks on this task and some neural network models have achieved comparable results using less artificial features. Since @cite_26 see the EDU segmentation task as a sequence labeling problem, we leverage the LSTM-CRF model @cite_47 @cite_29 which is proved effective in this problem.
@cite_38 @cite_47 @cite_26 @cite_9 @cite_29 @cite_44 @cite_27 @cite_46
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This paper presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods. In languages like English, there is a very small number of possible word forms with a given root word. However, languages like Turkish have very productive agglutinative morphology. Thus, it is an issue to build statistical models for specific tasks using the surface forms of the words, mainly because of the data sparseness problem. In order to alleviate this problem, we used additional syntactic information, i.e. the morphological structure of the words. We have successfully applied statistical methods using both the lexical and morphological information to sentence segmentation, topic segmentation, and name tagging tasks. For sentence segmentation, we have modeled the final inflectional groups of the words and combined it with the lexical model, and decreased the error rate to 4.34 , which is 21 better than the result obtained using only the surface forms of the words. For topic segmentation, stems of the words (especially nouns) have been found to be more effective than using the surface forms of the words and we have achieved 10.90 segmentation error rate on our test set according to the weighted TDT-2 segmentation cost metric. This is 32 better than the word-based baseline model. For name tagging, we used four different information sources to model names. Our first information source is based on the surface forms of the words. Then we combined the contextual cues with the lexical model, and obtained some improvement. After this, we modeled the morphological analyses of the words, and finally we modeled the tag sequence, and reached an F-Measure of 91.56 , according to the MUC evaluation criteria. Our results are important in the sense that, using linguistic information, i.e. morphological analyses of the words, and a corpus large enough to train a statistical model significantly improves these basic information extraction tasks for Turkish. Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which is both expensive and impractical to scale. Therefore, unsupervised semantic feature learning, i.e., learning without requiring manual annotation effort, is of crucial importance in order to successfully harvest the vast amount of visual data that are available today. In our work we propose to learn image features by training ConvNets to recognize the 2d rotation that is applied to the image that it gets as input. We demonstrate both qualitatively and quantitatively that this apparently simple task actually provides a very powerful supervisory signal for semantic feature learning. We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. Specifically, our results on those benchmarks demonstrate dramatic improvements w.r.t. prior state-of-the-art approaches in unsupervised representation learning and thus significantly close the gap with supervised feature learning. For instance, in PASCAL VOC 2007 detection task our unsupervised pre-trained AlexNet model achieves the state-of-the-art (among unsupervised methods) mAP of 54.4 that is only 2.4 points lower from the supervised case. We get similarly striking results when we transfer our unsupervised learned features on various other tasks, such as ImageNet classification, PASCAL classification, PASCAL segmentation, and CIFAR-10 classification. The code and models of our paper will be published on: this https URL . Neural network based approaches have recently produced record-setting performances in natural language understanding tasks such as word labeling. In the word labeling task, a tagger is used to assign a label to each word in an input sequence. Specifically, simple recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have shown to significantly outperform the previous state-of-the-art - conditional random fields (CRFs). This paper investigates using long short-term memory (LSTM) neural networks, which contain input, output and forgetting gates and are more advanced than simple RNN, for the word labeling task. To explicitly model output-label dependence, we propose a regression model on top of the LSTM un-normalized scores. We also propose to apply deep LSTM to the task. We investigated the relative importance of each gate in the LSTM by setting other gates to a constant and only learning particular gates. Experiments on the ATIS dataset validated the effectiveness of the proposed models. This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts (i.e., Weibo). We treat word segmentation as a character-wise sequence labeling problem, and explore two directions to enhance our CRF-based baseline. First, we employ a large-scale external lexicon for constructing extra lexicon features in the model, which is proven to be extremely useful. Second, we exploit two heterogeneous datasets, i.e., Penn Chinese Treebank 7 (CTB7) and People Daily (PD) to help word segmentation on Weibo. We adopt two mainstream approaches, i.e., the guide-feature based approach and the recently proposed coupled sequence labeling approach. We combine the above techniques in different ways and obtain four well-performing models. Finally, we merge the outputs of the four models and obtain the final results via Viterbi-based re-decoding. On the test data of Weibo, our proposed approach outperforms the baseline by (95.63-94.24=1.39 ) in terms of F1 score. Our final system rank the first place among five participants in the open track in terms of F1 score, and is also the best among all 28 submissions. All codes, experiment configurations, and the external lexicon are released at http: hlt.suda.edu.cn zhli.