Download Wikipedia Corpus
Maryalice Cutcher
I am using this model for word embeddings trained using word2vec, I want to get the embedding using GloVe to compare the performance. The model is trained using the English Wikipedia corpus, nevertheless, I have not found such a dataset online. Does anyone knows where can I find the dataset?
But this corpus allows you to search Wikipedia in a much more powerful way than is possible with the standard interface. You can search by word, phrase, part of speech, and synonyms. You can also find collocates (nearby words), and see re-sortable concordance lines for any word or phrase.
Most importantly, you can create and use virtual corpora from any of the 4,400,000 articles in the corpus. For example, you could create a corpus with 500-1,000 pages (perhaps 500,000-1,000,000 words) related to microbiology, economics, basketball, Buddhism, or thousands of other topics in less than a minute. (More information, with YouTube videos)
You can then search within that virtual corpus, compare the frequency of a word, phrase, or grammatical construction in your different virtual corpora, and also create "keyword lists" based on the texts in your virtual corpus.
Finally, the corpus is related to many other corpora of English that they have created. These corpora were formerly known as the "BYU Corpora", and they offer unparalleled insight into variation in English.
In linguistics and natural language processing, a corpus (pl.: corpora) or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated.
In order to make the corpora more useful for doing linguistic research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-speech tagging, or POS-tagging, in which information about each word's part of speech (verb, noun, adjective, etc.) is added to the corpus in the form of tags. Another example is indicating the lemma (base) form of each word. When the language of the corpus is not a working language of the researchers who use it, interlinear glossing is used to make the annotation bilingual.
Some corpora have further structured levels of analysis applied. In particular, smaller corpora may be fully parsed. Such corpora are usually called Treebanks or Parsed Corpora. The difficulty of ensuring that the entire corpus is completely and consistently annotated means that these corpora are usually smaller, containing around one to three million words. Other levels of linguistic structured analysis are possible, including annotations for morphology, semantics and pragmatics.
One of the first things required for natural language processing (NLP) tasks is a corpus. In linguistics and NLP, corpus (literally Latin for body) refers to a collection of texts. Such collections may be formed of a single language of texts, or can span multiple languages -- there are numerous reasons for which multilingual corpora (the plural of corpus) may be useful. Corpora may also consist of themed texts (historical, Biblical, etc.). Corpora are generally solely used for statistical linguistic analysis and hypothesis testing.
In order to easily build a text corpus void of the Wikipedia article markup, we will use gensim, a topic modeling library for Python. Specifically, the gensim.corpora.wikicorpus.WikiCorpus class is made just for this task:
I wrote a simple Python script (with inspiration from here) to build the corpus by stripping all Wikipedia markup from the articles, using gensim. You can read up on the WikiCorpus class (mentioned above) here.
The code is pretty straightforward: the Wikipedia dump file is opened and read article by article using the get_texts() method of the WikiCorpus class, all of which are ultimately written to a single text file. Both the Wikipedia dump file and the resulting corpus file must be specified on the command line.
And that's it. Some simple code to accomplish what gensim makes a simple task. Now that you are armed with an ample corpus, the natural language processing world is your oyster. Time for something fun.
I have this idea but I am not sure if it is correct, can I use the wikipedia data ONLY to train a RNN model, then use this trained model to train my training dataset for classification (as a transfer learning thing)?
A writ of habeas corpus (English: /ˌheɪbiəs ˈkɔːrpəs/; Latin: "may you have the body") protects people from being kept in jail or prison without a legal reason and without any end date. It is a writ (legal action) that says that if someone who was arrested or imprisoned wants to go to court to argue that they are being held illegally, the prison official must bring the individual to the court.[1] Once the person is brought before the court, the judge will decide if the person is being held lawfully, or has the right to be released.[1][2]
Given the number of remaining documents in a corpus, we need to choose n elements.The probability for the current element to be chosen is n / remaining. If we choose it, we just decreasethe n and move to the next element.
The filter function gets the entire context of the XML element passed into it,but you can of course choose not the use some or all parts of the context. Pleaserefer to gensim.corpora.wikicorpus.extract_pages() for the exact detailsof the page context.
The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
NOTE: Some known issues are reported in the README. If you fix them, find other bugs in the corpus, or are interested in improving and further developing the Wikicorpus or the Java Parser, please get in touch with Gemma Boleda.
Over the course of the next articles, I will show how to implement a Wikipedia article crawler, how to collect articles into a corpus, how to apply text preprocessing, tokenization, encoding and vectorization, and finally applying machine learning algorithms for clustering and classification.
The project starts with the creation of a custom Wikipedia crawler. Although we can work with Wikipedia corpus datasets from various sources, such as built-in corpus in NLTK, the custom crawler provides best control about file format, content, and the contents actuality.
Downloading and processing raw HTML can time consuming, especially when we also need to determine related links and categories from this. A very handy library comes to the rescue. The wikipedia-api does all of these heavy lifting for us. Based on this, lets develop the core features in a stepwise manner.
This method starts a timer to record how long the campus processing lasts, and then it uses the built-in methods of the corpus reader object and the just now created methods to compute the number of files, paragraphs, sentences, words, the vocabulary and the maximum number of words inside a document.
This article is the starting point for an NLP project to download, process, and apply machine learning algorithms on Wikipedia articles. Two aspects were covered in this article. First, the creation of the WikipediaReader class that finds articles by its name, and can extract its title, content, category and mentioned links. The crawler is controlled with two variables: The total number of crawled articles, and the depth of crawling. Second, the WikipediaCorpus, an extension of the NLTK PlaintextCorpusReader. This object provides convenient access to individual files, sentences, and words, as well as total corpus data like the number files or the vocabulary, the number of unique tokens. The next article continues with building a text processing pipeline.
GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.
The GloVe model is trained on the non-zero entries of a global word-word co-occurrence matrix, which tabulates how frequently words co-occur with one another in a given corpus. Populating this matrix requires a single pass through the entire corpus to collect the statistics. For large corpora, this pass can be computationally expensive, but it is a one-time up-front cost. Subsequent training iterations are much faster because the number of non-zero matrix entries is typically much smaller than the total number of words in the corpus.
GloVe is essentially a log-bilinear model with a weighted least-squares objective. The main intuition underlying the model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning. For example, consider the co-occurrence probabilities for target words ice and steam with various probe words from the vocabulary. Here are some actual probabilities from a 6 billion word corpus:
Corpus-hizkuntzalaritza hizkuntza "testu-errealetan" dauden adibideen arabera ikertzeaz arduratzen da. Metodo honek, lengoaia natural bat zuzentzen duten arau multzo abstraktuak inferitzen dituen ikuspegi bat aurkezten du, lengoaia horri dagozkion testuak aztertuz, gainera, lengoaia horrek beste lengoaia batzuekiko dituen harremanak ezartzen saiatzen da. Antzina testu-corpusak eskuz egiten ziren, baina gaur egun prozesu automatiko baten bidez eskuratzen dira gehienetan.
Filologiaren arloan corpusak ahozkoak zein idatzizkoak diren testuak eta hauek biltzen dituzten dokumentuak eratzen dituzte, era berean, testu guztiak behar bezala izan behar dira biltegiratuak. Corpus hauek hizkuntzalaritza aplikatuan erabiltzen diren ereduak osatzen dituzte, bestek beste, ikertzen ari denaren ezaugarriak ikertu eta analizatzeko. Corpus bat, corpus horrekin lortu nahi diren helburuen arabera definitu behar da.
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