Dependency tree structures capture longdistance and syntactic relationships between words in a sentence. The syntactic relations (e.g., nominal subject, object) can potentially infer the existence of certain named entities. In addition, the performance of a named entity recognizer could benefit from the longdistance dependencies between the words in dependency trees. In this work, we propose a simple yet effective dependency-guided LSTM-CRF model to encode the complete dependency trees and capture the above properties for the task of named entity recognition (NER). The data statistics show strong correlations between the entity types and dependency relations. We conduct extensive experiments on several standard datasets and demonstrate the effectiveness of the proposed model in improving NER and achieving state-of-theart performance. Our analysis reveals that the significant improvements mainly result from the dependency relations and long-distance interactions provided by dependency trees.

Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information. However, in practice, annotated data can often be imperfect with one typical issue being the training data may contain incomplete annotations. We highlight several pitfalls associated with learning under such a setup in the context of NER and identify limitations associated with existing approaches, proposing a novel yet easy-to-implement approach for recognizing named entities with incomplete data annotations. We demonstrate the effectiveness of our approach through extensive experiments. 1

The method of choice to study one-dimensional strongly interacting many body quantum systems is based on matrix product states and operators. Such method allows to explore the most relevant, and numerically manageable, portion of an exponentially large space. It also allows to describe accurately correlations between distant parts of a system, an important ingredient to account for the context in machine learning tasks. Here we introduce a machine learning model in which matrix product operators are trained to implement sequence to sequence prediction, i.e. given a sequence at a time step, it allows one to predict the next sequence. We then apply our algorithm to cellular automata (for which we show exact analytical solutions in terms of matrix product operators), and to nonlinear coupled maps. We show advantages of the proposed algorithm when compared to conditional random fields and bidirectional long short-term memory neural network. To highlight the flexibility of the algorithm, we also show that it can readily perform classification tasks.The last few years have witnessed a great shift of interest of society (individuals, industries and governmental organizations) towards machine learning and its wide range of applications ranging from images classification to translation and more.In recent years we have also witnessed an increasing activity at the intersection between machine learning and quantum physics. This includes further studies on quantum machine learning [1-3], use of machine learning for materials study [4], glassy physics [5], for phases recognition [6][7][8][9][10][11][12][13] and to numerically study quantum systems [14][15][16][17][18][19][20][21][22][23]. To be noted are studies on physical analogies and reasons for effectiveness of machine learning [24][25][26].Another research direction has been to apply tools developed in many body quantum physics to typical machine learning tasks. Recent examples are [27][28][29], where algorithms based on matrix product states (MPS), also known as tensor trains, were successfully used for supervised classification or unsupervised generative modelling. MPS based algorithms were also used for classification [30], as predictive modeling of stochastic processes [31], for language modeling [32] and compared to Boltzmann machines [33,34]. Usefuleness of tensor representations and matrix product states was also noted in [35].In physics, MPS are used to represent wave-functions, probability distributions or density matrices as a product of tensors [36][37][38][39][40]. Building on the density matrix renormalization group [41], matrix product states are very succesfully used in many body quantum physics to study ground or steady states and time evolution of Hamiltonian or dissipative systems [42][43][44][45][46][47][48]. As an extension, quantum mechanical operators are represented by matrix product operators (MPO), which are composed of tensors and map an MPS to another one. One important aspect of matrix product states is that they can allow to give an accurate ...

Billions of user-shared images are generated by individuals in many social networks today, and this particular form of user data is widely accessible to others due to the nature of online social sharing. When user social graphs are only accessible to exclusive parties, these user-shared images are proved to be an easier and effective alternative to discover user connections. This work investigated over 360 000 user shared images from two social networks, Skyrock and 163 Weibo, in which 3 million follower/followee relationships are involved. It is observed that the shared images from users with a follower/followee relationship show relatively higher similarities. A multimedia big data system that utilizes this observed phenomenon is proposed as an alternative to user-generated tags and social graphs for follower/followee recommendation and gender identification. To the best of our knowledge, this is the first attempt in this field to prove and formulate such a phenomenon for mass user-shared images along with more practical prediction methods. These findings are useful for information or services recommendations in any social network with intensive image sharing, as well as for other interesting personalization applications, particularly when there is no access to those exclusive user social graphs.