Natural language processing

Symbolic-to-symbolic transformations are used in various symbohc manipulations, including natural language processing and nrle-based system implementation. See Refs. 54 and 140. 

Feldman R, Regev Y, Hurvitz E, Finkelstein-Landau M. Mining the biomedical literature using semantic analysis and natural language processing techniques Biosilico 2003 1 69-80. 

The next steps consist of the extraction and normalization of terms from the zoned input document. To this end, we apply standard natural language processing techniques and normalize the extracted terms to their canonical form with string manipulations and morphological analysis. The former refers to the treatment of symbols (e.g., dashes), and the latter refers to variations of words due to inflection (e.g., plurals). These steps of information extraction rely on, and make extensive use of, our terminologies and ontologies. 

Me KEVITT, Paul, Sean 0 NUALLAIN and Conn Mulvihill (eds.) Language, Vision, and Music. Selected papers from the 8th International Workshop on the Cognitive Science of Natural Language Processing Galway, 1999. n.y.p. 

Jackson, P. and Moulinier, I. 2002. Natural Language Processing for Online Applications Text Retrieval, Extraction, and Categorization, Vol. 5. Philadelphia John Benjamin s Pubhshing Co. 

The first publications in this area were from the 1980s and 1990s.41 42 This area of research now uses the general principles of natural language processing (NLP) and, specifically, named entity extraction (NER) enhanced with specific developments for chemical and biochemical name recognition.43 44 Chapter 7 of this book is devoted to NLP and NER approaches applied to the extraction of chemical information, and we will not discuss these approaches in more detail here. 

Linking Chemical and Biological Information with Natural Language Processing... 

Some academic initiatives in the area of text mining and natural language processing have adopted the UIMA concept recently,72 and some commercial vendors of text mining solutions have done so, too.73 Of course, IBM itself is a major user of UIMA.74... 

Kim, J.-D., Ohta, T., Tsuruoka, Y., Tateisi, Y., Collier, N. 2004. Introduction to the Bio-Entity Recognition Task at JNLPBA. Proceedings of the International Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA-04), pp. 70-75. 

See http //en.wikipedia.org/wiki/Natural language processing... 

Rindflesh, T.C., Fiszman, M. 2003. The interaction of domain knowledge and linguistic structure in natural language processing interpreting hypemymic propositions in biomedical text. Journal of Biomedical Informatics. 36 462—477. 

Pathway analysis of gene expression data need not be restricted to only canonical pathways. Generally, the interactions described in the literature among gene products, proteins, and small molecules can be extracted from the literature and visualized as networks. Natural language processing (NLP)... 

Dyer, M. (1991). Symbolic neuroengineering for natural language processing A multilevel research approach. In J. A. Barnden J. B. Pollack (Eds.), Advances in connectionist and neural computation theory High-level connectionist models (Vol. 1, pp. 32-86). Norwood, NJ Ablex Publishing. 

Vol. 1083 K. Sparck Jones, J.R. Galliers, Evaluating Natural Language Processing Systems. XV, 228 pages. 1996. (Subseries LNAI). 

The intuition behind maximum entropy is that when we need to select among multiple possible distributions on a set of exclusive events, we choose the one that does not favor any of the events over the others. Hence, we choose the distribution that does not introduce new information that we did not have a priori. The principle of maximum entropy is widely used in other areas such as natural language processing. 

Practical natural language processing (NLP) tools are now available. 

Natural Language Processing Extraction Patterns for Protein-Protein Relationship... 

Note This table presents three ways that the "Raf phosphorvlales Mek relationship can be expressed in the literature. Of course, there are many other ways that this relationship can be presented in the text, and extraction patterns aimed at matching as many instances as possible have to be developed for a successful natural language processing application. 

Note Natural language processing can be thought of as syntactic/semantic querying. 

FIGURE 6.8 Interactive natural language processing. An example of the use of OBIIE. A general query is augmented by protein name expansions and protein-protein interaction ontology. The output allows for drill down to the specific sentence that meets the search criteria, and the context of the sentence is explored in the original document. 

---