By mining user-generated text content we can obtain musicrelated information that could not otherwise be extracted from audio signals or symbolic score representations. In this paper we propose a methodology for extracting music-related semantic information from an online discussion forum, rasikas.org, dedicated to the Carnatic music tradition. We first define a dictionary of relevant terms wit...

Multiple-choice reading comprehension (MCRC) aims to build an intelligent system that automatically selects answer from a candidate set when given passage and question. Existing MCRC systems rarely consider incorporating external knowledge such as explicit semantic information. In this work, we propose Contextual Semantic Fusion Network (CSFN) which effectively integrates contextual representat...

This paper addresses two important problems related to the use of semantics in IR. The first one concerns the representation of document semantics and its proper use in retrieval. The second is the integration of semantic-based retrieval with "traditional" keywords-based retrieval. The proposed approach aims to represent the document content by the best semantic network called document semantic...

Multilayered Extended Semantic Networks (abbreviated: MultiNet) are one of the few knowledge representation paradigms along the line of Semantic Networks (abbreviated: SN) with a comprehensive, systematic, and publicly available documentation. In contrast to logically oriented meaning representation systems with their extensional interpretation, MultiNet is based on a use-theoretic operational ...

Remote sensing scene classification aims to assign a specific semantic label remote image. Recently, convolutional neural networks have greatly improved the performance of classification. However, some confused images may be easily recognized as incorrect category, which generally degrade performance. The differences between image pairs can used distinguish categories. This letter proposed pair...

The emerging function of verb in ections in German language acquisition is modeled with a connectionist network. A network that is initially presented only with a semantic representation of sentences uses the in ectional verb ending -t to mark those sentences that are low in transitivity, whereas all other verb endings occur randomly. This behavior matches an early stage in German language acqu...

Korean Word Associations (KorWA) were collected to build a semantic network for the Korean language. A graphic representation approach of applying coefficients to complex networks allows us to discern the semantic structures within words. A semantic network of the KorWA was found to exhibit the scalefree property in its degree distribution. The growth of the network around hub words was also co...

This paper presents an attractor neural network model of semantic fact retrieval , based on Collins and Quillian's original semantic network models. In the context of modeling a semantic network, a distinction is made between associations linking together objects belonging to hierarchically-related semantic classes, and associations linking together objects and their attributes. Using a distrib...

Unsupervised image segmentation aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from view of feature alignments and uniformity for UISS models. We also make a comparison between image-wise representation learning. Based on analysis, argue that existing MI-based methods in suffer collapse. By...

Bayesian network is a probabilistic model to represent uncertainty available in knowledge base and using it tremendous works have been done to prove its relevance in uncertainty representation and reasoning using Bayesian inference. Probability can be used to represent uncertainty like prediction information, situational awareness, data and knowledge fusion etc in knowledge base to implement va...