This CRAN Task View contains a list of packages useful for natural language processing.

Phonetics and Speech Processing:

• emu is a collection of tools for the creation, manipulation, and analysis of speech databases. At the core of EMU is a database search engine which allows the researcher to find various speech segments based on the sequential and hierarchical structure of the utterances in which they occur. EMU includes an interactive labeller which can display spectrograms and other speech waveforms, and which allows the creation of hierarchical, as well as sequential, labels for a speech utterance.

Lexical Databases:

• wordnet provides an R interface to WordNet , a large lexical database of English.

Keyword Extraction and General String Manipulation:

• R's base package already provides a rich set of character manipulation routines. See help.search(keyword = "character", package = "base") for more information on these capabilities.
• RKEA provides an R interface to KEA (Version 5.0). KEA (for Keyphrase Extraction Algorithm) allows for extracting keyphrases from text documents. It can be either used for free indexing or for indexing with a controlled vocabulary.
• gsubfn can be used for certain parsing tasks such as extracting words from strings by content rather than by delimiters. demo("gsubfn-gries") shows an example of this in a natural language processing context.

Natural Language Processing:

• openNLP provides an R interface to OpenNLP , a collection of natural language processing tools including a sentence detector, tokenizer, pos-tagger, shallow and full syntactic parser, and named-entity detector, using the Maxent Java package for training and using maximum entropy models.
• openNLPmodels.en ships trained models for English and openNLPmodels.es for Spanish to be used with openNLP.
• RWeka is a interface to Weka which is a collection of machine learning algorithms for data mining tasks written in Java. Especially useful in the context of natural language processing is its functionality for tokenization and stemming.
• Snowball provides the Snowball stemmers which contain the Porter stemmer and several other stemmers for different languages. See the Snowball webpage for details.
• Alternatively, the Omegahat package Rstem provides an R interface to a C version of Porter's word stemming algorithm.

String Kernels:

• kernlab allows to create and compute with string kernels, like full string, spectrum, or bounded range string kernels. It can directly use the document format used by tm as input.

Text Mining:

• tm provides a comprehensive text mining framework for R. The Journal of Statistical Software article Text Mining Infrastructure in R gives a detailed overview and presents techniques for count-based analysis methods, text clustering, text classification and string kernels.
• lsa provides routines for performing a latent semantic analysis with R. The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome. The article Investigating Unstructured Texts with Latent Semantic Analysis gives a detailed overview and demonstrates the use of the package with examples from the are of technology-enhanced learning.
• corpora offers utility functions for the statistical analysis of corpus frequency data.
• languageR provides data sets and functions exemplifying statistical methods, and some facilitatory utility functions used in the book by R. H. Baayen: "Analyzing Linguistic Data: a Practical Introduction to Statistics Using R", Cambridge University Press, 2008.
• zipfR offers some statistical models for word frequency distributions. The utilities include functions for loading, manipulating and visualizing word frequency data and vocabulary growth curves. The package also implements several statistical models for the distribution of word frequencies in a population. (The name of this library derives from the most famous word frequency distribution, Zipf's law.)