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In this recipe, we"ll learn how to create our own categorized text corpus. in Software Make Data Matrix 2d barcode in Software In this recipe, we"ll learn how to create our own categorized text corpus.




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3. generate, create gs1 datamatrix barcode none on software projects Bar code to 2D Code ["adventure", "belle Software datamatrix 2d barcode s_lettres", "editorial", "fiction", "government", "hobbies", "humor", "learned", "lore", "mystery", "news", "religion", "reviews", "romance", "science_fiction"]. In this recipe, we"ll learn how to create our own categorized text corpus. Getting ready The easiest way to c ategorize a corpus is to have one file for each category. Following are two excerpts from the movie_reviews corpus:. movie_pos.txt the thin red line is flawed but it provokes . movie_neg.txt a big-budget and glo ssy production can not make up for a lack of spontaneity that permeates their tv show .. With these two files, we"ll have two categories: pos and neg. How to do it... We"ll use the Catego barcode data matrix for None rizedPlaintextCorpusReader, which inherits from both PlaintextCorpusReader and CategorizedCorpusReader. These two superclasses require three arguments: the root directory, the fileids, and a category specification..

>>> from nl tk.corpus.reader import CategorizedPlaintextCorpusReader >>> reader = CategorizedPlaintextCorpusReader(".

", r"movie_.*\. txt", cat_pattern=r"movie_(\w+)\.

txt") >>> reader.categories() ["neg", "pos"] >>> reader.fileids(categories=["neg"]) ["movie_neg.

txt"] >>> reader.fileids(categories=["pos"]) ["movie_pos.txt"].

Creating Custom Corpora How it works... The first two argume Data Matrix barcode for None nts to CategorizedPlaintextCorpusReader are the root directory and fileids, which are passed on to the PlaintextCorpusReader to read in the files. The cat_pattern keyword argument is a regular expression for extracting the category names from the fileids. In our case, the category is the part of the fileid after movie_ and before .

txt. The category must be surrounded by grouping parenthesis..

cat_pattern is passe d to CategorizedCorpusReader, which overrides the common corpus reader functions such as fileids(), words(), sents(), and paras() to accept a categories keyword argument. This way, you could get all the pos sentences by calling reader.sents(categories=["pos"]).

CategorizedCorpusReader also provides the categories() function, which returns a list of all known categories in the corpus. CategorizedPlaintextCorpusReader is an example of using multiple-inheritance to join methods from multiple superclasses, as shown in the following diagram:. There"s more... Instead of cat_patte rn, you could pass in a cat_map, which is a dictionary mapping a fileid to a list of category labels.. >>> reader Software Data Matrix ECC200 = CategorizedPlaintextCorpusReader(".", r"movie_.*\.

txt", cat_map={"movie_pos.txt": ["pos"], "movie_neg.txt": ["neg"]}) >>> reader.

categories() ["neg", "pos"]. 3 . Category file A third way of speci fying categories is to use the cat_file keyword argument to specify a filename containing a mapping of fileid to category. For example, the brown corpus has a file called cats.txt that looks like this:.

ca44 news cb01 editorial The reuters corpus h as files in multiple categories, and its cats.txt looks like this:. test/14840 rubber coffee lumber palm-oil veg-oil test/14841 wheat grain Categorized tagged corpus reader The brown corpus rea datamatrix 2d barcode for None der is actually an instance of CategorizedTaggedCorpusReader, which inherits from CategorizedCorpusReader and TaggedCorpusReader. Just like in CategorizedPlaintextCorpusReader, it overrides all the methods of TaggedCorpusReader to allow a categories argument, so you can call brown. tagged_sents(categories=["news"]) to get all the tagged sentences from the news category.

You can use the CategorizedTaggedCorpusReader just like CategorizedPlaintextCorpusReader for your own categorized and tagged text corpora.. Categorized corpora The movie_reviews co Software barcode data matrix rpus reader is an instance of CategorizedPlaintextCorpusReader, as is the reuters corpus reader. But where the movie_reviews corpus only has two categories (neg and pos), reuters has 90 categories. These corpora are often used for training and evaluating classifiers, which will be covered in 7, Text Classification.

. See also In the next recipe, Data Matrix barcode for None we"ll create a subclass of CategorizedCorpusReader and ChunkedCorpusReader for reading a categorized chunk corpus. Also see 7, Text Classification in which we use categorized text for classification..

Creating a categorized chunk corpus reader NLTK provides a Cate Software Data Matrix 2d barcode gorizedPlaintextCorpusReader and CategorizedTaggedCorpusReader, but there"s no categorized corpus reader for chunked corpora. So in this recipe, we"re going to make one..

Creating Custom Corpora Getting ready Refer to the earlier recipe, Creating a chunked phrase corpus, for an explanation of ChunkedCorpusReader, and to the previous recipe for details on CategorizedPlaintextCorpusReader and CategorizedTaggedCorpusReader, both of which inherit from CategorizedCorpusReader.. How to do it... We"ll create a class called CategorizedChunkedCorpusReader that inherits from both CategorizedCorpusReader and ChunkedCorpusReader. It is heavily based on the CategorizedTaggedCorpusReader, and also provides three additional methods for getting categorized chunks. The following code is found in catchunked.

py:. from nltk.corpus.rea DataMatrix for None der import CategorizedCorpusReader, ChunkedCorpusReader class CategorizedChunkedCorpusReader(CategorizedCorpusReader, ChunkedCorpusReader): def __init__(self, *args, **kwargs): CategorizedCorpusReader.

__init__(self, kwargs) ChunkedCorpusReader.__init__(self, *args, **kwargs) def _resolve(self, fileids, categories): if fileids is not None and categories is not None: raise ValueError("Specify fileids or categories, not both") if categories is not None: return self.fileids(categories) else: return fileids.

All of the following methods call the corresponding function in ChunkedCorpusReader with the value returned from _resolve(). We"ll start with the plain text methods..

def raw(self, fileid barcode data matrix for None s=None, categories=None): return ChunkedCorpusReader.raw(self, self._resolve(fileids, categories)) def words(self, fileids=None, categories=None): return ChunkedCorpusReader.

words(self, self._resolve(fileids, categories)) def sents(self, fileids=None, categories=None): return ChunkedCorpusReader.sents(self, self.

_resolve(fileids, categories)) def paras(self, fileids=None, categories=None):.
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