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Natural Language Processing This is a book about Natural Language Processing. By natural language we mean a language that is used for everyday communication by humans; languages like English, Hindi or Portuguese. In contrast to artificial languages such as programming languages and logical formalisms, natural languages have evolved as they pass from generation to generation, and are hard to pin down with explicit rules. We will take Natural Language Processing (or NLP for short) in a wide sense to cover any kind of computer manipulation of natural language.

Data journalism: 22 key links « Simon Rogers Simon Rogers Data journalism and other curiosities Search 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.

K Means Clustering with Tf-idf Weights Unsupervised learning algorithms in machine learning impose structure on unlabeled datasets. In Prof. Andrew Ng's inaugural ml-class from the pre-Coursera days, the first unsupervised learning algorithm introduced was k-means, which I implemented in Octave for programming exercise 7. ch07 For any given question, it's likely that someone has written the answer down somewhere. The amount of natural language text that is available in electronic form is truly staggering, and is increasing every day. However, the complexity of natural language can make it very difficult to access the information in that text. The state of the art in NLP is still a long way from being able to build general-purpose representations of meaning from unrestricted text. Sentiment Analysis The sentiment score is indexed on a normalized minus five (-5) to plus five (+5) scale. With this index, business users can roll up scores to preset categories, regions, or products, etc. to see sentiment trends across a brand, product, and/or company level. Tune for Precision. The process of indexing the relative negativity (or positivity) is scored at a word, phrase, and linguistic construct level. This enables the system to identify complex linguistic constructs like negation, capitalization of words and other visual indicators of tone. Our sentiment analysis interface enables your analysts to further refine our out-of-the-box models to:

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]

Introduction to Information Retrieval This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.

Related:  Datamining