Most previous research treats named entity extraction and classification as an end-to-end task. We argue that the two sub-tasks should be addressed separately. Entity extraction lies at the level of syntactic analysis while entity classification lies at the level of semantic analysis. According to Noam Chomsky's "Syntactic Structures," pp. 93-94 (Chomsky 1957), syntax is not appealed to semantics and semantics does not affect syntax. We analyze two benchmark datasets for the characteristics of named entities, finding that uncommon words can distinguish named entities from common text; where uncommon words are the words that hardly appear in common text and they are mainly the proper nouns. Experiments validate that lexical and syntactic features achieve state-of-the-art performance on entity extraction and that semantic features do not further improve the extraction performance, in both of our model and the state-of-the-art baselines. With Chomsky's view, we also explain the failure of joint syntactic and semantic parsings in other works.
In deep learning era, researchers use neural networks and word embeddings to develop variants of models on CoNLL03 dataset @cite_13 @cite_36 @cite_30 @cite_58 @cite_20 @cite_53 @cite_0 @cite_23 @cite_56 @cite_46 @cite_27 @cite_18 .
@cite_30 @cite_18 @cite_36 @cite_53 @cite_56 @cite_0 @cite_27 @cite_23 @cite_46 @cite_58 @cite_13 @cite_20
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Deep Neural Network has been applied to many Natural Language Processing tasks. Instead of building hand-craft features, DNN builds features by automatic learning, fitting different domains well. In this paper, we propose a novel convolution network, incorporating lexical features, applied to Relation Extraction. Since many current deep neural networks use word embedding by word table, which, however, neglects semantic meaning among words, we import a new coding method, which coding input words by synonym dictionary to integrate semantic knowledge into the neural network. We compared our Convolution Neural Network CNN on relation extraction with the state-of-art tree kernel approach, including Typed Dependency Path Kernel and Shortest Dependency Path Kernel and Context-Sensitive tree kernel, resulting in a 9 improvement competitive performance on ACE2005 data set. Also, we compared the synonym coding with the one-hot coding, and our approach got 1.6 improvement. Moreover, we also tried other coding method, such as hypernym coding, and give some discussion according the result. The success of neural network methods for computing word embeddings has motivated methods for generating semantic embeddings of longer pieces of text, such as sentences and paragraphs. Surprisingly, (ICLR'16) showed that such complicated methods are outperformed, especially in out-of-domain (transfer learning) settings, by simpler methods involving mild retraining of word embeddings and basic linear regression. The method of requires retraining with a substantial labeled dataset such as Paraphrase Database (, 2013). @PARASPLIT The current paper goes further, showing that the following completely unsupervised sentence embedding is a formidable baseline: Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA SVD. This weighting improves performance by about 10 to 30 in textual similarity tasks, and beats sophisticated supervised methods including RNN's and LSTM's. It even improves 's embeddings. This simple method should be used as the baseline to beat in future, especially when labeled training data is scarce or nonexistent. @PARASPLIT The paper also gives a theoretical explanation of the success of the above unsupervised method using a latent variable generative model for sentences, which is a simple extension of the model in (TACL'16) with new "smoothing" terms that allow for words occurring out of context, as well as high probabilities for words like and, not in all contexts. There has recently been a surge of research on neural network-inspired algorithms to produce numerical vector representations of words, based on contextual information. In this paper, we present an approach to improve such word embeddings by first mining cognitively salient word relationships from text and then using stochastic gradient descent to jointly optimize the embeddings to reflect this information, in addition to the regular contextual information captured by the word2vec CBOW objective. Our findings show that this new training regime leads to vectors that better reflect commonsense information about words. Many statistical models for natural language processing exist, including context-based neural networks that (1) model the previously seen context as a latent feature vector, (2) integrate successive words into the context using some learned representation (embedding), and (3) compute output probabilities for incoming words given the context. On the other hand, brain imaging studies have suggested that during reading, the brain (a) continuously builds a context from the successive words and every time it encounters a word it (b) fetches its properties from memory and (c) integrates it with the previous context with a degree of effort that is inversely proportional to how probable the word is. This hints to a parallelism between the neural networks and the brain in modeling context (1 and a), representing the incoming words (2 and b) and integrating it (3 and c). We explore this parallelism to better understand the brain processes and the neural networks representations. We study the alignment between the latent vectors used by neural networks and brain activity observed via Magnetoencephalography (MEG) when subjects read a story. For that purpose we apply the neural network to the same text the subjects are reading, and explore the ability of these three vector representations to predict the observed word-by-word brain activity. Our novel results show that: before a new word i is read, brain activity is well predicted by the neural network latent representation of context and the predictability decreases as the brain integrates the word and changes its own representation of context. Secondly, the neural network embedding of word i can predict the MEG activity when word i is presented to the subject, revealing that it is correlated with the brain’s own representation of word i. Moreover, we obtain that the activity is predicted in different regions of the brain with varying delay. The delay is consistent with the placement of each region on the processing pathway that starts in the visual cortex and moves to higher level regions. Finally, we show that the output probability computed by the neural networks agrees with the brain’s own assessment of the probability of word i, as it can be used to predict the brain activity after the word i’s properties have been fetched from memory and the brain is in the process of integrating it into the context. Deep learning has been applied for processing programs in recent years and gains extensive attention on the academic and industrial communities. In analogous to process natural language data based on word embeddings, embeddings of tokens (e.g. classes, variables, methods etc.) provide an important basis for processing programs with deep learning. Nowadays, lots of real-world programs rely on API libraries for implementation. They contain numbers of API tokens (e.g. API related classes, interfaces, methods etc.), which indicate notable semantics of programs. However, learning embeddings of API tokens is not exploited yet. In this paper, we propose a neural model to learn embeddings of API tokens. Our model combines a recurrent neural network with a convolutional neural network. And we use API documents as training corpus. Our model is trained on documents of five popular API libraries and evaluated on a description selecting task. To our best knowledge, this paper is the first to learn embeddings of API tokens and takes a meaningful step to facilitate deep learning based program processing. Deep neural networks have made significant breakthroughs in many fields of artificial intelligence. However, it has not been applied in the field of programming language processing. In this paper, we propose the tree-based convolutional neural network (TBCNN) to model programming languages, which contain rich and explicit tree structural information. In our model, program vector representations are learned by the "coding" pretraining criterion based on abstract syntax trees (ASTs); the convolutional layer explicitly captures neighboring features on the tree; with the "binary continuous tree" and "3-way pooling," our model can deal with ASTs of different shapes and sizes.We evaluate the program vector representations empirically, showing that the coding criterion successfully captures underlying features of AST nodes, and that program vector representations significantly speed up supervised learning. We also compare TBCNN to baseline methods; our model achieves better accuracy in the task of program classification. To our best knowledge, this paper is the first to analyze programs with deep neural networks; we extend the scope of deep learning to the field of programming language processing. The experimental results validate its feasibility; they also show a promising future of this new research area. Deep neural networks have witnessed great successes in various real applications, but it requires a large number of labeled data for training. In this paper, we propose tri-net, a deep neural network which is able to use massive unlabeled data to help learning with limited labeled data. We consider model initialization, diversity augmentation and pseudo-label editing simultaneously. In our work, we utilize output smearing to initialize modules, use fine-tuning on labeled data to augment diversity and eliminate unstable pseudo-labels to alleviate the influence of suspicious pseudolabeled data. Experiments show that our method achieves the best performance in comparison with state-of-the-art semi-supervised deep learning methods. In particular, it achieves 8:30 error rate on CIFAR- 10 by using only 4000 labeled examples. Machine learning algorithms based on deep neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. More specifically, we focus on classification of the well-known convolutional neural networks (CNN). First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train convolutional neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement convolutional neural networks over encrypted data and measure performance of the models. Our experimental results validate the soundness of our approach with several convolutional neural networks with varying number of layers and structures. When applied to the MNIST optical character recognition tasks, our approach achieves 99.52 accuracy which significantly outperforms the state-of-the-art solutions and is very close to the accuracy of the best non-private version, 99.77 . Also, it can make close to 164000 predictions per hour. We also applied our approach to CIFAR-10, which is much more complex compared to MNIST, and were able to achieve 91.5 accuracy with approximation polynomials used as activation functions. These results show that CryptoDL provides efficient, accurate and scalable privacy-preserving predictions. When analysing human activities using data mining or machine learning techniques, it can be useful to infer properties such as the gender or age of the people involved. This paper focuses on the sub-problem of gender recognition, which has been studied extensively in the literature, with two main problems remaining unsolved: how to improve the accuracy on real-world face images, and how to generalise the models to perform well on new datasets. We address these problems by collecting five million weakly labelled face images, and performing three different experiments, investigating: the performance difference between convolutional neural networks (CNNs) of differing depths and a support vector machine approach using local binary pattern features on the same training data, the effect of contextual information on classification accuracy, and the ability of convolutional neural networks and large amounts of training data to generalise to cross-database classification. We report record-breaking results on both the Labeled Faces in the Wild (LFW) dataset, achieving an accuracy of 98.90 , and the Images of Groups (GROUPS) dataset, achieving an accuracy of 91.34 for cross-database gender classification. In the last few years, Convolutional Neural Networks (CNNs) had become the default paradigm to address classification problems, specially, but not only, in image recognition. This is mainly due to the high success rate that they provide. Despite there currently exist approaches that apply deep learning to the 3D recognition problem, they are either too slow for online uses or too error prone. To fill this gap, we propose LonchaNet, a deep learning architecture for point clouds classification. Our system successfully achieves a high accuracy yet providing a low computation cost. A dense set of experiments were carried out in order to validate our system in the frame of the ModelNet — a large-scale 3D CAD models dataset — challenge. Our proposal achieves a success rate of 94.37 in the ModelNet-10 classification task, the second place in the leaderboard as of today (November, 2016). We introduce a novel, simple convolution neural network (CNN) architecture ‐ multi-group norm constraint CNN (MGNC-CNN) ‐ that capitalizes on multiple sets of word embeddings for sentence classification. MGNCCNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models. In the last few years, deep convolutional neural networks have become ubiquitous in computer vision, achieving state-of-the-art results on problems like object detection, semantic segmentation, and image captioning. However, they have not yet been widely investigated in the document analysis community. In this paper, we present a word spotting system based on convolutional neural networks. We train a network to extract a powerful image representation, which we then embed into a word embedding space. This allows us to perform word spotting using both query-by-string and query-by-example in a variety of word embedding spaces, both learned and handcrafted, for verbatim as well as semantic word spotting. Our novel approach is versatile and the evaluation shows that it outperforms the previous state-of-the-art for word spotting on standard datasets.