Sparse distributed representations as word embeddings for language understanding

Word embeddings are vector representations of words that capture semantic and syntactic similarities between them. Similar words tend to have closer vector representations in a N dimensional space considering, for instance, Euclidean distance between the points associated with the word vector representations in a continuous vector space. This property, makes word embeddings valuable in several Natural Language Processing tasks, from word analogy and similarity evaluation to the more complex text categorization, summarization or translation tasks. Typically state of the art word embeddings are dense vector representations, with low dimensionality varying from tens to hundreds of floating number dimensions, usually obtained from unsupervised learning on considerable amounts of text data by training and optimizing an objective function of a neural network. This work presents a methodology to derive word embeddings as binary sparse vectors, or word vector representations with high dimensionality, sparse representation and binary features (e.g. composed only by ones and zeros). The proposed methodology tries to overcome some disadvantages associated with state of the art approaches, namely the size of corpus needed for training the model, while presenting comparable evaluations in several Natural Language Processing tasks. Results show that high dimensionality sparse binary vectors representations, obtained from a very limited amount of training data, achieve comparable performances in similarity and categorization intrinsic tasks, whereas in analogy tasks good results are obtained only for nouns categories. Our embeddings outperformed eight state of the art word embeddings in word similarity tasks, and two word embeddings in categorization tasks.A designação word embeddings refere-se a representações vetoriais das palavras que capturam as similaridades semânticas e sintáticas entre estas. Palavras similares tendem a ser representadas por vetores próximos num espaço N dimensional considerando, por exemplo, a distância Euclidiana entre os pontos associados a estas representações vetoriais num espaço vetorial contínuo. Esta propriedade, torna as word embeddings importantes em várias tarefas de Processamento Natural da Língua, desde avaliações de analogia e similaridade entre palavras, às mais complexas tarefas de categorização, sumarização e tradução automática de texto. Tipicamente, as word embeddings são constituídas por vetores densos, de dimensionalidade reduzida. São obtidas a partir de aprendizagem não supervisionada, recorrendo a consideráveis quantidades de dados, através da otimização de uma função objetivo de uma rede neuronal. Este trabalho propõe uma metodologia para obter word embeddings constituídas por vetores binários esparsos, ou seja, representações vetoriais das palavras simultaneamente binárias (e.g. compostas apenas por zeros e uns), esparsas e com elevada dimensionalidade. A metodologia proposta tenta superar algumas desvantagens associadas às metodologias do estado da arte, nomeadamente o elevado volume de dados necessário para treinar os modelos, e simultaneamente apresentar resultados comparáveis em várias tarefas de Processamento Natural da Língua. Os resultados deste trabalho mostram que estas representações, obtidas a partir de uma quantidade limitada de dados de treino, obtêm performances consideráveis em tarefas de similaridade e categorização de palavras. Por outro lado, em tarefas de analogia de palavras apenas se obtém resultados consideráveis para a categoria gramatical dos substantivos. As word embeddings obtidas com a metodologia proposta, e comparando com o estado da arte, superaram a performance de oito word embeddings em tarefas de similaridade, e de duas word embeddings em tarefas de categorização de palavras

Random Projection in Deep Neural Networks

This work investigates the ways in which deep learning methods can benefit from random projection (RP), a classic linear dimensionality reduction method. We focus on two areas where, as we have found, employing RP techniques can improve deep models: training neural networks on high-dimensional data and initialization of network parameters. Training deep neural networks (DNNs) on sparse, high-dimensional data with no exploitable structure implies a network architecture with an input layer that has a huge number of weights, which often makes training infeasible. We show that this problem can be solved by prepending the network with an input layer whose weights are initialized with an RP matrix. We propose several modifications to the network architecture and training regime that makes it possible to efficiently train DNNs with learnable RP layer on data with as many as tens of millions of input features and training examples. In comparison to the state-of-the-art methods, neural networks with RP layer achieve competitive performance or improve the results on several extremely high-dimensional real-world datasets. The second area where the application of RP techniques can be beneficial for training deep models is weight initialization. Setting the initial weights in DNNs to elements of various RP matrices enabled us to train residual deep networks to higher levels of performance

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, \textbf{edge2vec}\ significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.Comment: 10 page

Toward Concept-Based Text Understanding and Mining

There is a huge amount of text information in the world, written in natural languages. Most of the text information is hard to access compared with other well-structured information sources such as relational databases. This is because reading and understanding text requires the ability to disambiguate text fragments at several levels, syntactically and semantically, abstracting away details and using background knowledge in a variety of ways. One possible solution to these problems is to implement a framework of concept-based text understanding and mining, that is, a mechanism of analyzing and integrating segregated information, and a framework of organizing, indexing, accessing textual information centered around real-world concepts. A fundamental difficulty toward this goal is caused by the concept ambiguity of natural language. In text, the real-world entities are referred using their names. The variability in writing a given concept, along with the fact that different concepts/enities may have very similar writings, poses a significant challenge to progress in text understanding and mining. Supporting concept-based natural language understanding requires resolving conceptual ambiguity, and in particular, identifying whether different mentions of real world entities, within and across documents, actually represent the same concept. This thesis systematically studies this fundamental problem. We study and propose different machine learning techniques to address different aspects of this problem and show that as more information can be exploited, the learning techniques developed accordingly, can continuously improve the identification accuracy. In addition, we extend our global probabilistic model to address a significant application -- semantic integration between text and databases

Proximal Methods for Hierarchical Sparse Coding

Sparse coding consists in representing signals as sparse linear combinations of atoms selected from a dictionary. We consider an extension of this framework where the atoms are further assumed to be embedded in a tree. This is achieved using a recently introduced tree-structured sparse regularization norm, which has proven useful in several applications. This norm leads to regularized problems that are difficult to optimize, and we propose in this paper efficient algorithms for solving them. More precisely, we show that the proximal operator associated with this norm is computable exactly via a dual approach that can be viewed as the composition of elementary proximal operators. Our procedure has a complexity linear, or close to linear, in the number of atoms, and allows the use of accelerated gradient techniques to solve the tree-structured sparse approximation problem at the same computational cost as traditional ones using the L1-norm. Our method is efficient and scales gracefully to millions of variables, which we illustrate in two types of applications: first, we consider fixed hierarchical dictionaries of wavelets to denoise natural images. Then, we apply our optimization tools in the context of dictionary learning, where learned dictionary elements naturally organize in a prespecified arborescent structure, leading to a better performance in reconstruction of natural image patches. When applied to text documents, our method learns hierarchies of topics, thus providing a competitive alternative to probabilistic topic models