Automatic Semantic Classification Research Articles

Automatic semantic classification of scientific literature according to the hallmarks of cancer.

The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research. We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future. The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html. simon.baker@cl.cam.ac.uk.

Cognitive informatics in image semantics description, identification and automatic pattern understanding

In the paper a new way of pattern semantic interpretation directed at automatic semantic classification and image content understanding will be described. Such an understanding will be based on the linguistic theories of pattern classification and is aimed at facilitation of content analysis for some classes of medical patterns. The approach presented in this paper will show the great possibilities of automatic disease interpretation in some analyzed data. The interpretation will be based on cognitive processes, which imitate the psychological processes of understanding the registered patterns as they take place in the brain of a human being. Cognitive analysis will be based on languages of shape description and picture grammars which allow the creation of syntactic descriptions of selected anatomic organs together with a definition of the semantic meaning of the changes in their shapes. Such descriptions will further allow creating a semantically-oriented representation of visual data describing pattern features which are useful in medical decisions and computer-aided diagnostic systems. This publication presents the cognitive systems of medical data image analysis. Systems that conduct cognitive analyses are designed so that semantic analyses can be used to interpret data and reconstruct medical images. These systems are built for medical types of data analysis systems. An analysis in classical CIAIS (Cognitive Image Analysis Information Systems) will be used to illustrate and present the method of learning new solutions which the systems have no knowledge of. The proposed solution presents the method of building a new class of cognitive systems names E-CIAIS (Extended Cognitive Image Analysis Information Systems).

Exploiting latent semantic information in statistical language modeling

Statistical language models used in large-vocabulary speech recognition must properly encapsulate the various constraints, both local and global, present in the language. While local constraints are readily captured through n-gram modeling, global constraints, such as long-term semantic dependencies, have been more difficult to handle within a data-driven formalism. This paper focuses on the use of latent semantic analysis, a paradigm that automatically uncovers the salient semantic relationships between words and documents in a given corpus. In this approach, (discrete) words and documents are mapped onto a (continuous) semantic vector space, in which familiar clustering techniques can be applied. This leads to the specification of a powerful framework for automatic semantic classification, as well as the derivation of several language model families with various smoothing properties. Because of their large-span nature, these language models are well suited to complement conventional n-grams. An integrative formulation is proposed for harnessing this synergy, in which the latent semantic information is used to adjust the standard n-gram probability. Such hybrid language modeling compares favorably with the corresponding n-gram baseline: experiments conducted on the Wall Street Journal domain show a reduction in average word error rate of over 20%. This paper concludes with a discussion of intrinsic tradeoffs, such as the influence of training data selection on the resulting performance.