Natural language processing

Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation. History[edit] The history of NLP generally starts in the 1950s, although work can be found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence. The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. Up to the 1980s, most NLP systems were based on complex sets of hand-written rules. NLP using machine learning[edit] Major tasks in NLP[edit] Parsing

The Global WordNet Associaton WordNet WordNet is a lexical database for the English language.[1] It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. The purpose is twofold: to produce a combination of dictionary and thesaurus that is more intuitively usable, and to support automatic text analysis and artificial intelligence applications. The database and software tools have been released under a BSD style license and can be downloaded and used freely. The database can also be browsed online. History and team members[edit] WordNet was created at the Cognitive Science Laboratory of Princeton University under the direction of psychology professor George Armitage Miller. Database contents[edit] Example entry "Hamburger" in WordNet good, right, ripe – (most suitable or right for a particular purpose; "a good time to plant tomatoes"; "the right time to act"; "the time is ripe for great sociological changes")

About WordNet - WordNet - About WordNet Automatic summarization Methods[edit] Methods of automatic summarization include extraction-based, abstraction-based, maximum entropy-based, and aided summarization. Extraction-based summarization[edit] Two particular types of summarization often addressed in the literature are keyphrase extraction, where the goal is to select individual words or phrases to "tag" a document, and document summarization, where the goal is to select whole sentences to create a short paragraph summary. Abstraction-based summarization[edit] Extraction techniques merely copy the information deemed most important by the system to the summary (for example, key clauses, sentences or paragraphs), while abstraction involves paraphrasing sections of the source document. While some work has been done in abstractive summarization (creating an abstract synopsis like that of a human), the majority of summarization systems are extractive (selecting a subset of sentences to place in a summary). Maximum entropy-based summarization[edit]

Snowball MALLET homepage MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document classification: efficient routines for converting text to “features”, a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. Quick Start / Developer’s Guide In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. Topic models are useful for analyzing large collections of unlabeled text. Many of the algorithms in MALLET depend on numerical optimization. The toolkit is Open Source Software, and is released under the Apache 2.0 License.

JGibbLDA: A Java Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for Parameter Estimation and Inference Topic Modeling Toolbox The first step in using the Topic Modeling Toolbox on a data file (CSV or TSV, e.g. as exported by Excel) is to tell the toolbox where to find the text in the file. This section describes how the toolbox converts a column of text from a file into a sequence of words. The process of extracting and preparing text from a CSV file can be thought of as a pipeline, where a raw CSV file goes through a series of stages that ultimately result in something that can be used to train the topic model. 01.val source = CSVFile("pubmed-oa-subset.csv") ~> IDColumn(1); 03.val tokenizer = { 04. 05. 06. 07. 10.val text = { 11. source ~> 12. 13. 14. 15. 16. 17. The input data file (in the source variable) is a pointer to the CSV file you downloaded earlier, which we will pass through a series of stages that each transform, filter, or otherwise interact with the data. If the first row in your CSV file contains the column names, you can remove that row using the Drop stage: Tokenizing Finding meaningful words