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Install on Windows, Mac, and Linux with one license.3 for 1 licensingYour personal license is good for up to 3 machines on any combination of platforms.Optional CloudServices (Win only)Sync and share all your settings, files, and code snippets across all your systems in a single click

In the Translate app , you can translate text, voice, and conversations between any of the supported languages. You can download languages to translate entirely on a device, even without an internet connection.

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.

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 useIt 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:

For a list of notaries public who hold active notary public commissions simply download this compressed file (ZIP). After decompressing the file, the file can be opened with any text editor. However, for optimum utility, import the active-notary.txt file into a database to permit better searching and sorting capability, since the number of records exceeds the maximum record limit for most spreadsheet programs.

ArcGIS Pro allows labeling highways with a shield marker symbol containing a highway number by using Structured Query Language (SQL) in the Label Class pane.In this example, the ROADS.shp file is used. This file can be downloaded from the Related Information section below.

They're generated the first time you play through a game and at that time cause noticeable stuttering. You can download a complete cache from places like here and rename them to match your game version's ID to spare yourself most of that unpleasant experience. Just keep in mind that shader caches from versions older than 1.8.0 are incompatible with later versions of Cemu.

The Wii U Gamepad offers an integrated touchscreen. You can access the Gamepad view by pressing and holding the [Tab] key on your keyboard. Clicking somewhere with the mouse simulates a touch on this screen.

Beautifully. Speech synthesis works by installing an app like Speechify either on your device or as a browser extension. AI scans the words on the page and reads it out loud, without any lag. You can change the default voice to a custom voice, change accents, languages, and even increase or decrease the speaking rate.

There are limitless reasons and use cases for TTS. Children pick up so much from listening (ask any parent) and unlocking the number of (quality) words a child can listen to holds tremendous potential in their development. College students, teachers, professors, parents, professionals, productivity enthusias