Maui automatically identifies main topics in text documents. Depending on the task, topics are tags, keywords, keyphrases, vocabulary terms, descriptors, index terms or titles of Wikipedia articles.

ReVerb is a program that automatically identifies and extracts binary relationships from English sentences. ReVerb is designed for Web-scale information extraction, where the target relations cannot be specified in advance and speed is important.

SentiWordNet is a lexical resource for opinion mining. SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, objectivity. SentiWordNet is described in details in the papers:

This site contains what we believe is the most accurate frequency data of English. It contains word frequency lists of the top 60,000 words (lemmas) in English, collocates lists (looking at nearby words to see word meaning and use), and n-grams (the frequency of all two and three-word sequences in the corpora).

Analyzing Text with the Natural Language Toolkit

The Corpus of Contemporary American English (COCA) is the largest freely-available corpus of English, and the only large and balanced corpus of American English. It was created by Mark Davies of Brigham Young University in 2008, and it is now used by tens of thousands of users every month (linguists, teachers, translators, and other researchers). COCA is also related to other large corpora that we have created or modified, including the British National Corpus (our architecture and interface), the 100 million word TIME Corpus (1920s-2000s), and the new 400 million word Corpus of Historical American English (COHA; 1810-2009).

The corpus contains more than 410 million words of text and is equally divided among spoken, fiction, popular magazines, newspapers, and academic texts. It includes 20 million words each year from 1990-2010 and the corpus is also updated once or twice a year (the most recent texts are from Summer 2010). Because of its design, it is perhaps the only corpus of English that is suitable for looking at current, ongoing changes in the language (see the 2010 article in Literary and Linguistic Computing).

Here are the datasets backing the Google Books Ngram Viewer. These datasets were generated in July 2009; we will update these datasets as our book scanning continues, and the updated versions will have distinct and persistent version identifiers (20090715 for the current set).

Each of the links below will directly download a fragment of the given corpus. For instance, the first hundred links below collectively comprise the 1-gram (i.e., individual words) counts for English, as collected from Google's scanned books around July 15, 2009. Details on the corpus construction can be found in the Science article written by J.B. Michel et al. but are abbreviated here.

YAGO2 is a huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames. Currently, YAGO2 has knowledge of more than 10 million entities (like persons, organizations, cities, etc.) and contains more than 80 million facts about these entities.

In YAGO2, we made an effort to treat time and location data as first-class citizen, extending the basic triple model by special fields for time and location for querying. Also, we took special care to consistently attach temporal and spatial data to all facts where it is semantically meaningful and where time and location can be derived from Wikipedia. Unlike many other automatically assembled knowledge bases, YAGO2 has a confirmed accuracy of 95%.

WordNet® is a large lexical database of English, developed under the direction of George A. Miller (Emeritus). Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser. WordNet is also freely and publicly available for download. WordNet's structure makes it a useful tool for computational linguistics and natural language processing.

Can I compute how frequently a word occurs in general English text? I have a list of about 2000 words, and I want to sort it with the most common words first.

CC licensed ebook on working with text