Classifying test executions automatically as pass or fail remains a key challenge in software testing and is referred to the oracle problem. It being attempted solve this problem with supervised learning over execution traces. A programme instrumented gather traces sequences of method invocations. small fraction programme's labelled verdicts. Execution are then embedded fixed length vectors neu...

The aim of this paper is to survey the feed-forward and self-organizing neural networks for the text document retrieval models, which retrieve text documents in a natural language. These models come from linguistic and conceptual approach of the text document analysis, where problems of document representation and document database creation are being solved. The proposed structure of the feed-f...

Owing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representative sentences from a spoken document to concisely express the most important theme of the document, has been an active area of research and experimentation. On the other hand, word embedding has emerged as a newly fa...

The paper starts with the need for classification. Then the reasons why neural networks are suitable for document classification are explained. The paper continues with the details of the most commonly used topologically organized network model proposed by Kohonen (1982), referred to as the self-organizing map (SOM). The general idea proposed is to display the contents of a document library by ...

The paper deals with text document retrieval from the given document collection by using neural networks, namely cascade neural network model, linear and nonlinear Hebbian neural networks and linear autoassociative neural network. With using neural networks it is possible to reduce the dimension of the search space with preserving the highest retrieval accuracy.

A shared bilingual word embedding space (SBWES) is an indispensable resource in a variety of cross-language NLP and IR tasks. A common approach to the SBWES induction is to learn a mapping function between monolingual semantic spaces, where the mapping critically relies on a seed word lexicon used in the learning process. In this work, we analyze the importance and properties of seed lexicons f...

Abstract Network embedding aims to map nodes in a network low-dimensional vector representations. Graph neural networks (GNNs) have received much attention and achieved state-of-the-art performance learning node representation. Using fundamental sociological theories (status theory balance theory) model signed networks, basing GNN on has become hot topic embedding. However, most GNNs fail use e...

We present an approach based on feed-forward neural networks for learning the distribution of textual documents. This approach is inspired by the Neural Autoregressive Distribution Estimator (NADE) model, which has been shown to be a good estimator of the distribution of discrete-valued high-dimensional vectors. In this paper, we present how NADE can successfully be adapted to the case of textu...