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The underlying concept that distinguishes man from woman, i.e. sex or gender, may be equivalently specified by various other word pairs, such as king and queen or brother and sister. To state this observation mathematically, we might expect that the vector differences man - woman, king - queen, and brother - sister might all be roughly equal. This property and other interesting patterns can be observed in the above set of visualizations.
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The tools provided in this package automate the collection and preparation of co-occurrence statistics for input into the model. The core training code is separated from these preprocessing steps and can be executed independently.
As one might expect, ice co-occurs more frequently with solid than it does with gas, whereas steam co-occurs more frequently with gas than it does with solid. Both words co-occur with their shared property water frequently, and both co-occur with the unrelated word fashion infrequently. Only in the ratio of probabilities does noise from non-discriminative words like water and fashioncancel out, so that large values (much greater than 1) correlate well with properties specific to ice, and small values (much less than 1) correlate well with properties specific of steam. In this way, the ratio of probabilities encodes some crude form of meaning associated with the abstract concept of thermodynamic phase.The training objective of GloVe is to learn word vectors such that their dot product equals the logarithm of the words' probability of co-occurrence. Owing to the fact that the logarithm of a ratio equals the difference of logarithms, this objective associates (the logarithm of) ratios of co-occurrence probabilities with vector differences in the word vector space. Because these ratios can encode some form of meaning, this information gets encoded as vector differences as well. For this reason, the resulting word vectors perform very well on word analogy tasks, such as those examined in the word2vec package.
Next, we need to obtain counts for each genre of interest. We'll useNLTK's support for conditional frequency distributions. These arepresented systematically in 2,where we also unpick the following code line by line. For the moment,you can ignore the details and just concentrate on the output.
Let's look at how the words America and citizen are used over time.The following codeconverts the words in the Inaugural corpusto lowercase using w.lower() ,then checks if they start with either of the "targets"america or citizen using startswith() .Thus it will count words like American's and Citizens.We'll learn about conditional frequency distributions in2; for now just considerthe output, shown in 1.1.
Many text corpora contain linguistic annotations, representing POS tags,named entities, syntactic structures, semantic roles, and so forth. NLTK providesconvenient ways to access several of these corpora, and has data packages containing corporaand corpus samples, freely downloadable for use in teaching and research.1.2 lists some of the corpora. For information aboutdownloading them, see more examples of how to access NLTK corpora,please consult the Corpus HOWTO at
By this time you've probably typed and retyped a lot of code in the Pythoninteractive interpreter. If you mess up when retyping a complex example you haveto enter it again. Using the arrow keys to access and modify previous commands is helpful but only goes sofar. In this section we see two important ways to reuse code: text editors and Python functions.
From now on, you have a choice of using the interactive interpreter or atext editor to create your programs. It is often convenient to test your ideasusing the interpreter, revising a line of code until it does what you expect.Once you're ready, you can paste the code(minus any >>> or ... prompts) into the text editor,continue to expand it, and finally save the programin a file so that you don't have to type it in again later.Give the file a short but descriptive name, using all lowercase letters and separatingwords with underscore, and using the .py filename extension, e.g., monty_python.py.
Rather than repeating the same code several times over, it is moreefficient and reliable to localize this work inside a function.A function is just a named block of code that performs some well-definedtask, as we saw in 1.A function is usually defined to take some inputs, using special variables known as parameters,and it may produce a result, also known as a return value.We define a function using the keyword def followed by thefunction name and any input parameters, followed by the body of thefunction. Here's the function we saw in 1(including the import statement that is needed for Python 2, in order to make division behave as expected):
We use the keyword return to indicate the value that isproduced as output by the function. In the above example,all the work of the function is done in the return statement.Here's an equivalent definition which does the same workusing multiple lines of code. We'll change the parameter namefrom text to my_text_data to remind you that this is an arbitrary choice:
Over time you will find that you create a variety of useful little text processing functions,and you end up copying them from old programs to new ones. Which file contains thelatest version of the function you want to use?It makes life a lot easier if you can collect your work into a single place, andaccess previously defined functions without making copies.
A collection of variable and function definitions in a file is called a Pythonmodule. A collection of related modules is called a package.NLTK's code for processing the Brown Corpus is an example of a module,and its collection of code for processing all the different corpora isan example of a package. NLTK itself is a set of packages, sometimescalled a library.
It is well known that names ending in the letter a are almost always female.We can see this and some other patterns in the graph in 4.4,produced by the following code. Remember that name[-1] is the last letterof name.
The above program scans the lexicon looking for entries whose pronunciation consists ofthree phones . If the condition is true, it assigns the contentsof pron to three new variables ph1, ph2 and ph3. Notice the unusualform of the statement which does that work .
Rather than iterating over the whole dictionary, we can also access itby looking up particular words. We will use Python's dictionary datastructure, which we will study systematically in 3.We look up a dictionary by giving its name followed by a key(such as the word 'fire') inside square brackets .
Another example of a tabular lexicon is the comparative wordlist.NLTK includes so-called Swadesh wordlists, lists of about 200 common wordsin several languages. The languages are identified using an ISO 639 two-letter code.
Perhaps the single most popular tool used by linguists for managing datais Toolbox, previously known as Shoebox since it replacesthe field linguist's traditional shoebox full of file cards.Toolbox is freely downloadable from
WordNet is a semantically-oriented dictionary of English,similar to a traditional thesaurus but with a richer structure.NLTK includes the English WordNet, with 155,287 wordsand 117,659 synonym sets. We'll begin bylooking at synonyms and how they are accessed in WordNet.
Of course we know that whale is very specific (and baleen whale even more so),while vertebrate is more general and entity is completely general.We can quantify this concept of generality by looking up the depth of each synset:
This page presents an HTML-coded version of the nineteenth-century edition of the originalmedieval French text of the tournament book. For more information about the sources of thisedition, see About this Translation.
I have got downloaded a file that got downloaded in a format .pynb.txt extension. Can anyone help me to figure how to make it in a readable format?Attaching a screenshot of the file when i tried opening in python notebook.
CMD in Windows machine type jupyter notebookThen Opened new IPY kernalIn the new IPY Kernal went to file>>Download as>> Notebook(.ipynb)This will create a blank .ipynb fileopened that file in notepad and replaced the code.
OSIS Bible Tool Library Using the OSIS XML format, this online Bible Tool library with many Bible versions, multiple languages, commentaries, devotionals, lexicons, and dictionaries. Displayed in a browsable listing at crosswire.org/study and data sets can be downloaded as SWORD modules.
Using real viruses for testing in the real world is rather like setting fire to the dustbin in your office to see whether the smoke detector is working. Such a test will give meaningful results, but with unappealing, unacceptable risks.
If you remember the very first step of this process, we had to download a zip file of Shakespearean text. What happens if you email someone your top-shake-words.sh and they try to run it without having first downloaded the Shakespearean text files?
Since unzip shakespeare.zip creates a new directory named shakespeare-plays-flat-text, we need to modify our script to read files from that subdirectory (previously, we changed into the subdirectory, but that's an unnecessary step). Here's the lines we add and change so that top-shake-words.sh downloads the data before acting on it:
So now top-shake-words.sh will conveniently download shakespeare-plays-flat-text.zip and unzip it for the user. That's nice. But what happens if the user already ran the script once? Well, unfortunately, top-shake-words.sh, as we've modified it, will always re-download the data, even if it already exists. Try running it again to see what happens.
This ability to modularize your code will be profoundly helpful as you do more complicated tasks. Sometimes, you'll find yourself writing scripts that call other scripts, so that you don't have any one mega-script that is impossible to re-read and debug. In fact, you are already doing this: did you write the cat command? Or grep? No. Their functionality has been wrapped up in such a way that you just have to remember the names of their commands. 041b061a72