Text graph

According to our article, classrooms are full of students with behavioral problems that make education difficult and ineffective. Also, this category of students often suffers from poor self-awareness, lack of self-discipline, inability to express their feelings, and sometimes they experience low emotional intelligence such as: anxiety, sadness, and anger. In addition, the student, anything that affects his emotional state, can affect his academic performance. For example, if the student feels anxious about passing the test and fears that his parents will punish him, he will become very tense, and this will make him lose the ability to focus. Therefore, it is very important for the student to organize and understand his feelings and control them in order to be able to cross all the difficulties that he is going through during the education period.

Representation

The semantics of what a text graph's nodes and edges represent can vary widely. Nodes for example can simply connect to tokenized words, or to domain-specific terms, or to entities mentioned in the text. The edges, on the other hand, can be between these text-based tokens or they can also link to a knowledge base.

TextGraphs Workshop series

The TextGraphs Workshop series is a series of regular academic workshops intended to encourage the synergy between the fields of natural language processing (NLP) and graph theory. The mix between the two started small, with graph theoretical framework providing efficient and elegant solutions for NLP applications that focused on single documents for part-of-speech tagging, word sense disambiguation and semantic role labelling, got progressively larger with ontology learning and information extraction from large text collections.

The 11th edition of the workshop (TextGraphs-11) will be collocated with the Annual Meeting of Association for Computational Linguistics (ACL 2017) in Vancouver, BC, Canada.

Areas of interest

• Graph-based methods for providing reasoning and interpretation of deep learning methods
  • Graph-based methods for reasoning and interpreting deep processing by neural networks,
  • Explorations of the capabilities and limits of graph-based methods applied to neural networks in general
  • Investigation of which aspects of neural networks are not susceptible to graph-based methods.
• Graph-based methods for Information Retrieval, Information Extraction, and Text Mining
  • Graph-based methods for word sense disambiguation,
  • Graph-based representations for ontology learning,
  • Graph-based strategies for semantic relations identification,
  • Encoding semantic distances in graphs,
  • Graph-based techniques for text summarization, simplification, and paraphrasing
  • Graph-based techniques for document navigation and visualization
  • Reranking with graphs
  • Applications of label propagation algorithms, etc.
• New graph-based methods for NLP applications
  • Random walk methods in graphs
  • Spectral graph clustering
  • Semi-supervised graph-based methods
  • Methods and analyses for statistical networks
  • Small world graphs
  • Dynamic graph representations
  • Topological and pretopological analysis of graphs
  • Graph kernels, etc.
• Graph-based methods for applications on social networks
  • Rumor proliferation
  • E-reputation
  • Multiple identity detection
  • Language dynamics studies
  • Surveillance systems, etc.
• Graph-based methods for NLP and Semantic Web
  • Representation learning methods for knowledge graphs (i.e., knowledge graph embedding)
  • Using graphs-based methods to populate ontologies using textual data,
  • Inducing knowledge of ontologies into NLP applications using graphs,
  • Merging ontologies with graph-based methods using NLP techniques.

See also

• Bag-of-words model
• Document classification
• Document-term matrix
• Hyperlinking
• Graph database
• Wiki

External links

• Gabor Melli's page on text graphs Description of text graphs from a semantic processing perspective.