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We've seen that the addition and multiplication operations apply to strings, not just numbers. However, note that we cannot use subtraction or division with strings:.

These error messages are another example of Python telling us that we have got our data types in a muddle. In the first case, we are told that the operation of subtraction i.

So far, when we have wanted to look at the contents of a variable or see the result of a calculation, we have just typed the variable name into the interpreter.

We can also see the contents of a variable using the print statement:. Notice that there are no quotation marks this time. When we inspect a variable by typing its name in the interpreter, the interpreter prints the Python representation of its value.

Since it's a string, the result is quoted. However, when we tell the interpreter to print the contents of the variable, we don't see quotation characters since there are none inside the string.

The print statement allows us to display more than one item on a line in various ways, as shown below:. As we saw in 2 for lists, strings are indexed, starting from zero.

When we index a string, we get one of its characters or letters. A single character is nothing special — it's just a string of length 1. Again as with lists, we can use negative indexes for strings, where -1 is the index of the last character.

Positive and negative indexes give us two ways to refer to any position in a string. In this case, when the string had a length of 12, indexes 5 and -7 both refer to the same character a space.

We can write for loops to iterate over the characters in strings. We can count individual characters as well.

We should ignore the case distinction by normalizing everything to lowercase, and filter out non-alphabetic characters:. This gives us the letters of the alphabet, with the most frequently occurring letters listed first this is quite complicated and we'll explain it more carefully below.

You might like to visualize the distribution using fdist. The relative character frequencies of a text can be used in automatically identifying the language of the text.

The string "Monty Python" is shown along with its positive and negative indexes; two substrings are selected using "slice" notation. The slice [m,n] contains the characters from position m through n A substring is any continuous section of a string that we want to pull out for further processing.

We can easily access substrings using the same slice notation we used for lists see 3. For example, the following code accesses the substring starting at index 6 , up to but not including index Here we see the characters are 'P' , 'y' , 't' , and 'h' which correspond to monty[6] This is because a slice starts at the first index but finishes one before the end index.

We can also slice with negative indexes — the same basic rule of starting from the start index and stopping one before the end index applies; here we stop before the space character.

As with list slices, if we omit the first value, the substring begins at the start of the string. If we omit the second value, the substring continues to the end of the string:.

We test if a string contains a particular substring using the in operator, as follows:. We can also find the position of a substring within a string, using find:.

Make up a sentence and assign it to a variable, e. Now write slice expressions to pull out individual words. This is obviously not a convenient way to process the words of a text!

Python has comprehensive support for processing strings. A summary, including some operations we haven't seen yet, is shown in 3. For more information on strings, type help str at the Python prompt.

Strings and lists are both kinds of sequence. We can pull them apart by indexing and slicing them, and we can join them together by concatenating them.

However, we cannot join strings and lists:. When we open a file for reading into a Python program, we get a string corresponding to the contents of the whole file.

If we use a for loop to process the elements of this string, all we can pick out are the individual characters — we don't get to choose the granularity.

By contrast, the elements of a list can be as big or small as we like: So lists have the advantage that we can be flexible about the elements they contain, and correspondingly flexible about any downstream processing.

Consequently, one of the first things we are likely to do in a piece of NLP code is tokenize a string into a list of strings 3.

Conversely, when we want to write our results to a file, or to a terminal, we will usually format them as a string 3.

Lists and strings do not have exactly the same functionality. Lists have the added power that you can change their elements:. On the other hand if we try to do that with a string — changing the 0th character in query to 'F' — we get:.

This is because strings are immutable — you can't change a string once you have created it. However, lists are mutable , and their contents can be modified at any time.

As a result, lists support operations that modify the original value rather than producing a new value. Consolidate your knowledge of strings by trying some of the exercises on strings at the end of this chapter.

Our programs will often need to deal with different languages, and different character sets. The concept of "plain text" is a fiction. In this section, we will give an overview of how to use Unicode for processing texts that use non-ASCII character sets.

Unicode supports over a million characters. Each character is assigned a number, called a code point. Within a program, we can manipulate Unicode strings just like normal strings.

However, when Unicode characters are stored in files or displayed on a terminal, they must be encoded as a stream of bytes.

Some encodings such as ASCII and Latin-2 use a single byte per code point, so they can only support a small subset of Unicode, enough for a single language.

Other encodings such as UTF-8 use multiple bytes and can represent the full range of Unicode characters. Text in files will be in a particular encoding, so we need some mechanism for translating it into Unicode — translation into Unicode is called decoding.

Conversely, to write out Unicode to a file or a terminal, we first need to translate it into a suitable encoding — this translation out of Unicode is called encoding , and is illustrated in 3.

From a Unicode perspective, characters are abstract entities which can be realized as one or more glyphs. Only glyphs can appear on a screen or be printed on paper.

A font is a mapping from characters to glyphs. Let's assume that we have a small text file, and that we know how it is encoded.

This file is encoded as Latin-2, also known as ISO The Python open function can read encoded data into Unicode strings, and write out Unicode strings in encoded form.

It takes a parameter to specify the encoding of the file being read or written. So let's open our Polish file with the encoding 'latin2' and inspect the contents of the file:.

We find the integer ordinal of a character using ord. The hexadecimal 4 digit notation for is type hex to discover this , and we can define a string with the appropriate escape sequence.

There are many factors determining what glyphs are rendered on your screen. If you are sure that you have the correct encoding, but your Python code is still failing to produce the glyphs you expected, you should also check that you have the necessary fonts installed on your system.

It may be necessary to configure your locale to render UTF-8 encoded characters, then use print nacute.

The module unicodedata lets us inspect the properties of Unicode characters. In the following example, we select all characters in the third line of our Polish text outside the ASCII range and print their UTF-8 byte sequence, followed by their code point integer using the standard Unicode convention i.

If you replace c. Alternatively, you may need to replace the encoding 'utf8' in the example by 'latin2' , again depending on the details of your system.

The next examples illustrate how Python string methods and the re module can work with Unicode characters.

We will take a close look at the re module in the following section. If you are used to working with characters in a particular local encoding, you probably want to be able to use your standard methods for inputting and editing strings in a Python file.

Many linguistic processing tasks involve pattern matching. For example, we can find words ending with ed using endswith 'ed'. We saw a variety of such "word tests" in 4.

Regular expressions give us a more powerful and flexible method for describing the character patterns we are interested in. There are many other published introductions to regular expressions, organized around the syntax of regular expressions and applied to searching text files.

Instead of doing this again, we focus on the use of regular expressions at different stages of linguistic processing. As usual, we'll adopt a problem-based approach and present new features only as they are needed to solve practical problems.

In our discussion we will mark regular expressions using chevrons like this: To use regular expressions in Python we need to import the re library using: We also need a list of words to search; we'll use the Words Corpus again 4.

We will preprocess it to remove any proper names. We will use the re. We need to specify the characters of interest, and use the dollar sign which has a special behavior in the context of regular expressions in that it matches the end of the word:.

Suppose we have room in a crossword puzzle for an 8-letter word with j as its third letter and t as its sixth letter.

In place of each blank cell we use a period:. We could count the total number of occurrences of this word in either spelling in a text using sum 1 for w in text if re.

The T9 system is used for entering text on mobile phones see 3. Two or more words that are entered with the same sequence of keystrokes are known as textonyms.

For example, both hole and golf are entered by pressing the sequence What other words could be produced with the same sequence?

The third and fourth characters are also constrained. Only four words satisfy all these constraints. Look for some "finger-twisters", by searching for words that only use part of the number-pad.

Notice that it can be applied to individual letters, or to bracketed sets of letters:. Notice this includes non-alphabetic characters.

Here are some more examples of regular expressions being used to find tokens that match a particular pattern, illustrating the use of some new symbols: You probably worked out that a backslash means that the following character is deprived of its special powers and must literally match a specific character in the word.

The pipe character indicates a choice between the material on its left or its right. Parentheses indicate the scope of an operator: The meta-characters we have seen are summarized in 3.

To the Python interpreter, a regular expression is just like any other string. If the string contains a backslash followed by particular characters, it will interpret these specially.

In general, when using regular expressions containing backslash, we should instruct the interpreter not to look inside the string at all, but simply to pass it directly to the re library for processing.

We do this by prefixing the string with the letter r , to indicate that it is a raw string. If you get into the habit of using r ' The above examples all involved searching for words w that match some regular expression regexp using re.

Apart from checking if a regular expression matches a word, we can use regular expressions to extract material from words, or to modify words in specific ways.

Let's find all the vowels in a word, then count them:. Let's look for all sequences of two or more vowels in some text, and determine their relative frequency:.

Once we can use re. It is sometimes noted that English text is highly redundant, and it is still easy to read when word-internal vowels are left out.

For example, declaration becomes dclrtn , and inalienable becomes inlnble , retaining any initial or final vowel sequences. The regular expression in our next example matches initial vowel sequences, final vowel sequences, and all consonants; everything else is ignored.

This three-way disjunction is processed left-to-right, if one of the three parts matches the word, any later parts of the regular expression are ignored.

Next, let's combine regular expressions with conditional frequency distributions. Here we will extract all consonant-vowel sequences from the words of Rotokas, such as ka and si.

Since each of these is a pair, it can be used to initialize a conditional frequency distribution. We then tabulate the frequency of each pair:.

Examining the rows for s and t , we see they are in partial "complementary distribution", which is evidence that they are not distinct phonemes in the language.

Thus, we could conceivably drop s from the Rotokas alphabet and simply have a pronunciation rule that the letter t is pronounced s when followed by i.

Note that the single entry having su , namely kasuari , 'cassowary' is borrowed from English. If we want to be able to inspect the words behind the numbers in the above table, it would be helpful to have an index, allowing us to quickly find the list of words that contains a given consonant-vowel pair, e.

Here's how we can do this:. In the case of the word kasuari , it finds ka , su and ri. One further step, using nltk. Index , converts this into a useful index.

When we use a web search engine, we usually don't mind or even notice if the words in the document differ from our search terms in having different endings.

A query for laptops finds documents containing laptop and vice versa. Indeed, laptop and laptops are just two forms of the same dictionary word or lemma.

For some language processing tasks we want to ignore word endings, and just deal with word stems. There are various ways we can pull out the stem of a word.

Here's a simple-minded approach which just strips off anything that looks like a suffix:. Although we will ultimately use NLTK's built-in stemmers, it's interesting to see how we can use regular expressions for this task.

Our first step is to build up a disjunction of all the suffixes. We need to enclose it in parentheses in order to limit the scope of the disjunction.

This is because the parentheses have a second function, to select substrings to be extracted. If we want to use the parentheses to specify the scope of the disjunction, but not to select the material to be output, we have to add?: Here's the revised version.

However, we'd actually like to split the word into stem and suffix. So we should just parenthesize both parts of the regular expression:.

This looks promising, but still has a problem. Let's look at a different word, processes:. The regular expression incorrectly found an -s suffix instead of an -es suffix.

This demonstrates another subtlety: This works even when we allow an empty suffix, by making the content of the second parentheses optional:.

This approach still has many problems can you spot them? Notice that our regular expression removed the s from ponds but also from is and basis.

It produced some non-words like distribut and deriv , but these are acceptable stems in some applications. You can use a special kind of regular expression for searching across multiple words in a text where a text is a list of tokens.

The angle brackets are used to mark token boundaries, and any whitespace between the angle brackets is ignored behaviors that are unique to NLTK's findall method for texts.

The second example finds three-word phrases ending with the word bro. The last example finds sequences of three or more words starting with the letter l.

Consolidate your understanding of regular expression patterns and substitutions using nltk. For more practice, try some of the exercises on regular expressions at the end of this chapter.

It is easy to build search patterns when the linguistic phenomenon we're studying is tied to particular words. In some cases, a little creativity will go a long way.

For instance, searching a large text corpus for expressions of the form x and other ys allows us to discover hypernyms cf With enough text, this approach would give us a useful store of information about the taxonomy of objects, without the need for any manual labor.

However, our search results will usually contain false positives, i. For example, the result: Nevertheless, we could construct our own ontology of English concepts by manually correcting the output of such searches.

This combination of automatic and manual processing is the most common way for new corpora to be constructed. We will return to this in Searching corpora also suffers from the problem of false negatives, i.

It is risky to conclude that some linguistic phenomenon doesn't exist in a corpus just because we couldn't find any instances of a search pattern.

Perhaps we just didn't think carefully enough about suitable patterns. Look for instances of the pattern as x as y to discover information about entities and their properties.

In earlier program examples we have often converted text to lowercase before doing anything with its words, e. By using lower , we have normalized the text to lowercase so that the distinction between The and the is ignored.

Often we want to go further than this, and strip off any affixes, a task known as stemming. A further step is to make sure that the resulting form is a known word in a dictionary, a task known as lemmatization.

We discuss each of these in turn. First, we need to define the data we will use in this section:. NLTK includes several off-the-shelf stemmers, and if you ever need a stemmer you should use one of these in preference to crafting your own using regular expressions, since these handle a wide range of irregular cases.

The Porter and Lancaster stemmers follow their own rules for stripping affixes. Observe that the Porter stemmer correctly handles the word lying mapping it to lie , while the Lancaster stemmer does not.

Stemming is not a well-defined process, and we typically pick the stemmer that best suits the application we have in mind.

The Porter Stemmer is a good choice if you are indexing some texts and want to support search using alternative forms of words illustrated in 3.

Indexing a Text Using a Stemmer. The WordNet lemmatizer only removes affixes if the resulting word is in its dictionary.

This additional checking process makes the lemmatizer slower than the above stemmers. Notice that it doesn't handle lying , but it converts women to woman.

The WordNet lemmatizer is a good choice if you want to compile the vocabulary of some texts and want a list of valid lemmas or lexicon headwords.

Another normalization task involves identifying non-standard words including numbers, abbreviations, and dates, and mapping any such tokens to a special vocabulary.

For example, every decimal number could be mapped to a single token 0. An example is the Mr. That was once a popular model, but discontinued.

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Easy food dehydrator recipes for the healthy and sometimes lazy. Since its first episode, Raw has broadcast live from different arenas in cities and towns in eleven different nations the United States , Canada , the United Kingdom , Afghanistan in , Iraq in and , South Africa , [4] Germany , [5] Japan , [6] Italy , [7] and Mexico.

Following the th episode on July 23, , Raw became a three-hour broadcast from two hours, a format that had previously been reserved for special episodes.

The original Raw was sixty minutes in length and broke new ground in televised professional wrestling. Traditionally, wrestling shows were taped on sound stages with small audiences or at large arena shows.

The Raw formula was considerably different from the taped weekend shows that aired at the time such as Superstars and Wrestling Challenge.

Instead of matches taped weeks in advance with studio voice overs and taped discussion, Raw was a show shot and aired to a live audience, with angles playing out as they happened.

The combination of an intimate venue and live action proved to be a successful improvement. However, the weekly live schedule proved to be a financial drain on the WWF.

From Spring up until Spring , Raw would tape several week's worth of episodes after a live episode had aired.

The first episode produced outside of New York was taped in Bushkill , Pennsylvania in November and Raw left the Manhattan Center permanently as the show would be taken on the road throughout the United States and had in smaller venues.

At the start of the ratings war in through to mid, Raw and Nitro exchanged victories over each other in a closely contested rivalry.

Beginning in mid, however, due to the nWo angle , Nitro started a ratings win-streak that lasted for 84 consecutive weeks, ending on April 13, It was also during the time Raw would be aired live more often 1 live raw, followed by 1 taped raw.

On orders from Bischoff, Nitro announcer Tony Schiavone gave away this previously taped result on a live Nitro and then sarcastically added, "That's gonna put some butts in the seats", consequently resulting in over , viewers switching channels to Raw Is War to see the underdog capture the WWF Championship.

In March , as a result of the overabundance of talent left over from the Invasion storyline, WWF instituted a process known as the " brand extension ", under which Raw and SmackDown would be treated as two distinct divisions, each with their own rosters and championships.

On the August 29, episode of Raw , it was announced that performers from Raw and SmackDown were no longer exclusive to their respective brand, thus effectively dissolving the brand extension.

Furthermore, the broadcast table was moved to the entrance ramp similar to how it was in — Strowman won the Gauntlet match by pinning The Miz in what was the longest match in WWE history, lasting nearly two hours.

Raw ' s original set featured red, white, and blue ring-ropes, a blue ring-apron, blue steps, and a small stage made of neon tubes.

Since March 10, , broadcasts of Raw were split into two hours and given hourly names for television ratings purposes , with the first hour being referred to as Raw Is War and the second as War Zone in television listings, and by the show's on-screen graphics beginning with the June 9, episode.

In , the entrance way was changed to feature "Raw" in giant letters. They also updated the stage to feature a large screen known as the TitanTron.

Beginning October 1, , in direct response to the September 11 attacks , the first hour was referred to as Raw instead of Raw Is War and the second hour changed from the War Zone to the Raw Zone by the show's on-screen graphics; however, announcers would generally refer to the entire two-hour block as Raw on-air.

Raw updated to a new, industrial-inspired, parallelogram -shaped TitanTron in When the War ended, they began advertising their website on the ring aprons instead.

They occasionally used black ropes. Like the previous set, the TNN logo was relocated to the bottom side of the TitanTron which was then replaced by the Spike TV logo on June 16, upon network relaunch.

On October 3, , as Raw returned to USA Network, the set was retained but the beams and lighting on the sides were modified.

No changes to the Raw set beyond October 9, when it unveiled the new logo and opening intro featuring " However, the logo and intro was retained until November 9, even with the changeover to high-definition broadcast on January 21, , replacing the previous set which the "Minitron" was destroyed by Triple H when Vince McMahon's face was shown.

In , Raw went HD debuting a new stage. In , WWE retired the red ropes for Raw after thirteen years for an all white scheme, and in became standard for all WWE programming.

In , Raw updated their HD set. Starting in mid , this set would also be featured in pay-per-views. Komen organization for Breast Cancer Awareness Month.

WWE is one of many organizations who provide financial contributions as well as getting customers and employees to support the cause. On August 18, , Raw switched to a full In conjunction with this, Raw updated its graphics package, with the new WWE logo first used with the WWE Network's launch in February now on the lower-right corner of the screen, right next to the word, "Live".

Also, the new WWE logo is seen on the ring's turnbuckle covers. The USA Network logo has also been moved to the lower-left hand corner of the screen.

Also, Raw ' s theme song "The Night" was modified. On the 1,th episode of Raw , "The Night" by Kromestatik debuted as the theme for Raw while "Energy" by Shinedown served as the secondary theme-song until August 18, , when it was replaced with "Denial" by We Are Harlot.

On the September 14, season premiere of Raw , the middle rope was colored gold. Throughout the month of October , the WWE announcer table, entrance ramp and ring skirts were co-branded with Susan G.

Komen for the Cure of Breast cancer. On July 25, , the ropes returned to red, the announce table moved back to the top of the stage for the first time since , and a new HD set and graphics were debuted.

The new set was almost identical to the set used for SummerSlam and It received some negativity after the newly dubbed "New Era" was using an older set.

The set was revamped just four weeks later with a more elaborate and distinctive design. The new set features the absence of a traditional TitanTron which had been custom since On the January 29, episode of Raw, new graphics and an updated logo were introduced.

In August , the court ruled in favor of Muscle Flex, Inc. In a press release date issued on July 20, , Muscle Flex Inc. In previous quarters, these numbers were even higher.

Throughout its broadcast history, the show has aired episodes that have different themes. Some of them are yearly events such as the Slammy Awards.

Others include tributes to various professional wrestlers who have recently died or retired from actively performing, as well as episodes commemorating various show milestones or anniversaries.

The show features various on-air personalities including the wrestlers themselves both males and females , ring announcers, commentators, and on-screen authority figures.

Raw also has had various recurring on-air segments hosted by members of the roster.

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Regular expressions give us a more powerful and flexible method for describing the character patterns we are interested in.

There are many other published introductions to regular expressions, organized around the syntax of regular expressions and applied to searching text files.

Instead of doing this again, we focus on the use of regular expressions at different stages of linguistic processing.

As usual, we'll adopt a problem-based approach and present new features only as they are needed to solve practical problems.

In our discussion we will mark regular expressions using chevrons like this: To use regular expressions in Python we need to import the re library using: We also need a list of words to search; we'll use the Words Corpus again 4.

We will preprocess it to remove any proper names. We will use the re. We need to specify the characters of interest, and use the dollar sign which has a special behavior in the context of regular expressions in that it matches the end of the word:.

Suppose we have room in a crossword puzzle for an 8-letter word with j as its third letter and t as its sixth letter. In place of each blank cell we use a period:.

We could count the total number of occurrences of this word in either spelling in a text using sum 1 for w in text if re.

The T9 system is used for entering text on mobile phones see 3. Two or more words that are entered with the same sequence of keystrokes are known as textonyms.

For example, both hole and golf are entered by pressing the sequence What other words could be produced with the same sequence?

The third and fourth characters are also constrained. Only four words satisfy all these constraints. Look for some "finger-twisters", by searching for words that only use part of the number-pad.

Notice that it can be applied to individual letters, or to bracketed sets of letters:. Notice this includes non-alphabetic characters.

Here are some more examples of regular expressions being used to find tokens that match a particular pattern, illustrating the use of some new symbols: You probably worked out that a backslash means that the following character is deprived of its special powers and must literally match a specific character in the word.

The pipe character indicates a choice between the material on its left or its right. Parentheses indicate the scope of an operator: The meta-characters we have seen are summarized in 3.

To the Python interpreter, a regular expression is just like any other string. If the string contains a backslash followed by particular characters, it will interpret these specially.

In general, when using regular expressions containing backslash, we should instruct the interpreter not to look inside the string at all, but simply to pass it directly to the re library for processing.

We do this by prefixing the string with the letter r , to indicate that it is a raw string. If you get into the habit of using r ' The above examples all involved searching for words w that match some regular expression regexp using re.

Apart from checking if a regular expression matches a word, we can use regular expressions to extract material from words, or to modify words in specific ways.

Let's find all the vowels in a word, then count them:. Let's look for all sequences of two or more vowels in some text, and determine their relative frequency:.

Once we can use re. It is sometimes noted that English text is highly redundant, and it is still easy to read when word-internal vowels are left out.

For example, declaration becomes dclrtn , and inalienable becomes inlnble , retaining any initial or final vowel sequences.

The regular expression in our next example matches initial vowel sequences, final vowel sequences, and all consonants; everything else is ignored.

This three-way disjunction is processed left-to-right, if one of the three parts matches the word, any later parts of the regular expression are ignored.

Next, let's combine regular expressions with conditional frequency distributions. Here we will extract all consonant-vowel sequences from the words of Rotokas, such as ka and si.

Since each of these is a pair, it can be used to initialize a conditional frequency distribution. We then tabulate the frequency of each pair:. Examining the rows for s and t , we see they are in partial "complementary distribution", which is evidence that they are not distinct phonemes in the language.

Thus, we could conceivably drop s from the Rotokas alphabet and simply have a pronunciation rule that the letter t is pronounced s when followed by i.

Note that the single entry having su , namely kasuari , 'cassowary' is borrowed from English. If we want to be able to inspect the words behind the numbers in the above table, it would be helpful to have an index, allowing us to quickly find the list of words that contains a given consonant-vowel pair, e.

Here's how we can do this:. In the case of the word kasuari , it finds ka , su and ri. One further step, using nltk. Index , converts this into a useful index.

When we use a web search engine, we usually don't mind or even notice if the words in the document differ from our search terms in having different endings.

A query for laptops finds documents containing laptop and vice versa. Indeed, laptop and laptops are just two forms of the same dictionary word or lemma.

For some language processing tasks we want to ignore word endings, and just deal with word stems. There are various ways we can pull out the stem of a word.

Here's a simple-minded approach which just strips off anything that looks like a suffix:. Although we will ultimately use NLTK's built-in stemmers, it's interesting to see how we can use regular expressions for this task.

Our first step is to build up a disjunction of all the suffixes. We need to enclose it in parentheses in order to limit the scope of the disjunction.

This is because the parentheses have a second function, to select substrings to be extracted. If we want to use the parentheses to specify the scope of the disjunction, but not to select the material to be output, we have to add?: Here's the revised version.

However, we'd actually like to split the word into stem and suffix. So we should just parenthesize both parts of the regular expression:. This looks promising, but still has a problem.

Let's look at a different word, processes:. The regular expression incorrectly found an -s suffix instead of an -es suffix.

This demonstrates another subtlety: This works even when we allow an empty suffix, by making the content of the second parentheses optional:.

This approach still has many problems can you spot them? Notice that our regular expression removed the s from ponds but also from is and basis.

It produced some non-words like distribut and deriv , but these are acceptable stems in some applications. You can use a special kind of regular expression for searching across multiple words in a text where a text is a list of tokens.

The angle brackets are used to mark token boundaries, and any whitespace between the angle brackets is ignored behaviors that are unique to NLTK's findall method for texts.

The second example finds three-word phrases ending with the word bro. The last example finds sequences of three or more words starting with the letter l.

Consolidate your understanding of regular expression patterns and substitutions using nltk. For more practice, try some of the exercises on regular expressions at the end of this chapter.

It is easy to build search patterns when the linguistic phenomenon we're studying is tied to particular words. In some cases, a little creativity will go a long way.

For instance, searching a large text corpus for expressions of the form x and other ys allows us to discover hypernyms cf With enough text, this approach would give us a useful store of information about the taxonomy of objects, without the need for any manual labor.

However, our search results will usually contain false positives, i. For example, the result: Nevertheless, we could construct our own ontology of English concepts by manually correcting the output of such searches.

This combination of automatic and manual processing is the most common way for new corpora to be constructed. We will return to this in Searching corpora also suffers from the problem of false negatives, i.

It is risky to conclude that some linguistic phenomenon doesn't exist in a corpus just because we couldn't find any instances of a search pattern.

Perhaps we just didn't think carefully enough about suitable patterns. Look for instances of the pattern as x as y to discover information about entities and their properties.

In earlier program examples we have often converted text to lowercase before doing anything with its words, e. By using lower , we have normalized the text to lowercase so that the distinction between The and the is ignored.

Often we want to go further than this, and strip off any affixes, a task known as stemming. A further step is to make sure that the resulting form is a known word in a dictionary, a task known as lemmatization.

We discuss each of these in turn. First, we need to define the data we will use in this section:. NLTK includes several off-the-shelf stemmers, and if you ever need a stemmer you should use one of these in preference to crafting your own using regular expressions, since these handle a wide range of irregular cases.

The Porter and Lancaster stemmers follow their own rules for stripping affixes. Observe that the Porter stemmer correctly handles the word lying mapping it to lie , while the Lancaster stemmer does not.

Stemming is not a well-defined process, and we typically pick the stemmer that best suits the application we have in mind.

The Porter Stemmer is a good choice if you are indexing some texts and want to support search using alternative forms of words illustrated in 3.

Indexing a Text Using a Stemmer. The WordNet lemmatizer only removes affixes if the resulting word is in its dictionary. This additional checking process makes the lemmatizer slower than the above stemmers.

Notice that it doesn't handle lying , but it converts women to woman. The WordNet lemmatizer is a good choice if you want to compile the vocabulary of some texts and want a list of valid lemmas or lexicon headwords.

Another normalization task involves identifying non-standard words including numbers, abbreviations, and dates, and mapping any such tokens to a special vocabulary.

For example, every decimal number could be mapped to a single token 0. This keeps the vocabulary small and improves the accuracy of many language modeling tasks.

Tokenization is the task of cutting a string into identifiable linguistic units that constitute a piece of language data.

Although it is a fundamental task, we have been able to delay it until now because many corpora are already tokenized, and because NLTK includes some tokenizers.

Now that you are familiar with regular expressions, you can learn how to use them to tokenize text, and to have much more control over the process.

The very simplest method for tokenizing text is to split on whitespace. Consider the following text from Alice's Adventures in Wonderland:. We could split this raw text on whitespace using raw.

Other whitespace characters, such as carriage-return and form-feed should really be included too. The above statement can be rewritten as re.

Remember to prefix regular expressions with the letter r meaning "raw" , which instructs the Python interpreter to treat the string literally, rather than processing any backslashed characters it contains.

Splitting on whitespace gives us tokens like ' not' and 'herself,'. Observe that this gives us empty strings at the start and the end to understand why, try doing 'xx'.

We get the same tokens, but without the empty strings, with re. Now that we're matching the words, we're in a position to extend the regular expression to cover a wider range of cases.

This means that punctuation is grouped with any following letters e. We need to include?: We'll also add a pattern to match quote characters so these are kept separate from the text they enclose.

For readability we break up the regular expression over several lines and add a comment about each line. When set to True , the regular expression specifies the gaps between tokens, as with re.

We can evaluate a tokenizer by comparing the resulting tokens with a wordlist, and reporting any tokens that don't appear in the wordlist, using set tokens.

You'll probably want to lowercase all the tokens first. Tokenization turns out to be a far more difficult task than you might have expected.

No single solution works well across-the-board, and we must decide what counts as a token depending on the application domain.

When developing a tokenizer it helps to have access to raw text which has been manually tokenized, in order to compare the output of your tokenizer with high-quality or "gold-standard" tokens.

A final issue for tokenization is the presence of contractions, such as didn't. If we are analyzing the meaning of a sentence, it would probably be more useful to normalize this form to two separate forms: We can do this work with the help of a lookup table.

This section discusses more advanced concepts, which you may prefer to skip on the first time through this chapter. Tokenization is an instance of a more general problem of segmentation.

In this section we will look at two other instances of this problem, which use radically different techniques to the ones we have seen so far in this chapter.

Manipulating texts at the level of individual words often presupposes the ability to divide a text into individual sentences. As we have seen, some corpora already provide access at the sentence level.

In the following example, we compute the average number of words per sentence in the Brown Corpus:.

In other cases, the text is only available as a stream of characters. Before tokenizing the text into words, we need to segment it into sentences.

Here is an example of its use in segmenting the text of a novel. Note that if the segmenter's internal data has been updated by the time you read this, you will see different output:.

Notice that this example is really a single sentence, reporting the speech of Mr Lucian Gregory. However, the quoted speech contains several sentences, and these have been split into individual strings.

This is reasonable behavior for most applications. Sentence segmentation is difficult because period is used to mark abbreviations, and some periods simultaneously mark an abbreviation and terminate a sentence, as often happens with acronyms like U.

For another approach to sentence segmentation, see 2. For some writing systems, tokenizing text is made more difficult by the fact that there is no visual representation of word boundaries.

For example, in Chinese, the three-character string: A similar problem arises in the processing of spoken language, where the hearer must segment a continuous speech stream into individual words.

A particularly challenging version of this problem arises when we don't know the words in advance. This is the problem faced by a language learner, such as a child hearing utterances from a parent.

Consider the following artificial example, where word boundaries have been removed:. Our first challenge is simply to represent the problem: We can do this by annotating each character with a boolean value to indicate whether or not a word-break appears after the character an idea that will be used heavily for "chunking" in 7.

Let's assume that the learner is given the utterance breaks, since these often correspond to extended pauses. Here is a possible representation, including the initial and target segmentations:.

Observe that the segmentation strings consist of zeros and ones. They are one character shorter than the source text, since a text of length n can only be broken up in n-1 places.

The segment function in 3. Reconstruct Segmented Text from String Representation: Now the segmentation task becomes a search problem: We assume the learner is acquiring words and storing them in an internal lexicon.

Given a suitable lexicon, it is possible to reconstruct the source text as a sequence of lexical items. Following Brent, , we can define an objective function , a scoring function whose value we will try to optimize, based on the size of the lexicon number of characters in the words plus an extra delimiter character to mark the end of each word and the amount of information needed to reconstruct the source text from the lexicon.

We illustrate this in 3. Calculation of Objective Function: Given a hypothetical segmentation of the source text on the left , derive a lexicon and a derivation table that permit the source text to be reconstructed, then total up the number of characters used by each lexical item including a boundary marker and the number of lexical items used by each derivation, to serve as a score of the quality of the segmentation; smaller values of the score indicate a better segmentation.

It is a simple matter to implement this objective function, as shown in 3. The final step is to search for the pattern of zeros and ones that minimizes this objective function, shown in 3.

Notice that the best segmentation includes "words" like thekitty , since there's not enough evidence in the data to split this any further.

As this search algorithm is non-deterministic, you may see a slightly different result. With enough data, it is possible to automatically segment text into words with a reasonable degree of accuracy.

Such methods can be applied to tokenization for writing systems that don't have any visual representation of word boundaries. Often we write a program to report a single data item, such as a particular element in a corpus that meets some complicated criterion, or a single summary statistic such as a word-count or the performance of a tagger.

More often, we write a program to produce a structured result; for example, a tabulation of numbers or linguistic forms, or a reformatting of the original data.

When the results to be presented are linguistic, textual output is usually the most natural choice. However, when the results are numerical, it may be preferable to produce graphical output.

In this section you will learn about a variety of ways to present program output. The simplest kind of structured object we use for text processing is lists of words.

When we want to output these to a display or a file, we must convert these lists into strings. To do this in Python we use the join method, and specify the string to be used as the "glue".

Many people find this notation for join counter-intuitive. The join method only works on a list of strings — what we have been calling a text — a complex type that enjoys some privileges in Python.

The print command yields Python's attempt to produce the most human-readable form of an object. The second method — naming the variable at a prompt — shows us a string that can be used to recreate this object.

It is important to keep in mind that both of these are just strings, displayed for the benefit of you, the user. They do not give us any clue as to the actual internal representation of the object.

There are many other useful ways to display an object as a string of characters. This may be for the benefit of a human reader, or because we want to export our data to a particular file format for use in an external program.

Formatted output typically contains a combination of variables and pre-specified strings, e. Print statements that contain alternating variables and constants can be difficult to read and maintain.

Another solution is to use string formatting. To understand what is going on here, let's test out the format string on its own.

By now this will be your usual method of exploring new syntax. A string containing replacement fields is called a format string.

We can have any number of placeholders, but the str. Arguments to format are consumed left to right, and any superfluous arguments are simply ignored.

The field name in a format string can start with a number, which refers to a positional argument of format. We can also provide the values for the placeholders indirectly.

Here's an example using a for loop:. So far our format strings generated output of arbitrary width on the page or screen. We can add padding to obtain output of a given width by inserting into the brackets a colon ': An important use of formatting strings is for tabulating data.

Recall that in 1 we saw data being tabulated from a conditional frequency distribution. Let's perform the tabulation ourselves, exercising full control of headings and column widths, as shown in 3.

Note the clear separation between the language processing work, and the tabulation of results. Recall from the listing in 3.

This allows us to specify the width of a field using a variable. We have seen how to read text from files 3. It is often useful to write output to files as well.

The following code opens a file output. When we write non-text data to a file we must convert it to a string first.

We can do this conversion using formatting strings, as we saw above. Let's write the total number of words to our file:. You should avoid filenames that contain space characters like output file.

When the output of our program is text-like, instead of tabular, it will usually be necessary to wrap it so that it can be displayed conveniently.

Consider the following output, which overflows its line, and which uses a complicated print statement:. We can take care of line wrapping with the help of Python's textwrap module.

For maximum clarity we will separate each step onto its own line:. Notice that there is a linebreak between more and its following number.

If we wanted to avoid this, we could redefine the formatting string so that it contained no spaces, e. Extra materials for this chapter are posted at http: Remember to consult the Python reference materials at http: For example, this documentation covers "universal newline support," explaining how to work with the different newline conventions used by various operating systems.

For more extensive discussion of text processing with Python see Mertz, For information about normalizing non-standard words see Sproat et al, There are many references for regular expressions, both practical and theoretical.

For a comprehensive and detailed manual in using regular expressions, covering their syntax in most major programming languages, including Python, see Friedl, Other presentations include Section 2.

There are many online resources for Unicode. Useful discussions of Python's facilities for handling Unicode are:. Our method for segmenting English text follows Brent, ; this work falls in the area of language acquisition Niyogi, Collocations are a special case of multiword expressions.

A multiword expression is a small phrase whose meaning and other properties cannot be predicted from its words alone, e.

Simulated annealing is a heuristic for finding a good approximation to the optimum value of a function in a large, discrete search space, based on an analogy with annealing in metallurgy.

The technique is described in many Artificial Intelligence texts. The approach to discovering hyponyms in text using search patterns like x and other ys is described by Hearst, Write a Python statement that changes this to "colourless" using only the slice and concatenation operations.

For example, 'dogs' [: Use slice notation to remove the affixes from these words we've inserted a hyphen to indicate the affix boundary, but omit this from your strings: Is it possible to construct an index that goes too far to the left, before the start of the string?

The following returns every second character within the slice: It also works in the reverse direction: Explain why this is a reasonable result.

Use from urllib import request and then request. Define a function load f that reads from the file named in its sole argument, and returns a string containing the text of the file.

Now, split raw on some character other than space, such as 's'. What happens when the string being split contains tab characters, consecutive space characters, or a sequence of tabs and spaces?

What is the difference? Try converting between strings and integers using int "3" and str 3. Next, start up a new session with the Python interpreter, and enter the expression monty at the prompt.

You will get an error from the interpreter. Now, try the following note that you have to leave off the. This time, Python should return with a value.

You can also try import prog , in which case Python should be able to evaluate the expression prog. Print them in order.

Are any words duplicated in this list, because of the presence of case distinctions or punctuation? Read the file into a Python list using open filename.

Next, break each line into its two fields using split , and convert the number into an integer using int. The result should be a list of the form: For example, access a weather site and extract the forecast top temperature for your town or city today.

In order to do this, extract all substrings consisting of lowercase letters using re. Try to categorize these words manually and discuss your findings.

You will see that there is still a fair amount of non-textual data there, particularly Javascript commands. You may also find that sentence breaks have not been properly preserved.

Define further regular expressions that improve the extraction of text from this web page. Explain why this regular expression won't work: Normalize the text to lowercase before converting it.

Add more substitutions of your own. Now try to map s to two different values: Each word of the text is converted as follows: Hungarian , extract the vowel sequences of words, and create a vowel bigram table.

Write a generator expression that produces a sequence of randomly chosen letters drawn from the string "aehh " , and put this expression inside a call to the ''.

You should get a result that looks like uncontrolled sneezing or maniacal laughter: Use split and join again to normalize the whitespace in this string.

The corresponding free cortisol fractions in these sera were 4. Should we say that the numeric expression 4. Or should we say that it's a single compound word?

Or should we say that it is actually nine words, since it's read "four point five three, plus or minus zero point fifteen percent"? Or should we say that it's not a "real" word at all, since it wouldn't appear in any dictionary?

Discuss these different possibilities. Can you think of application domains that motivate at least two of these answers?

Compute the ARI score for various sections of the Brown Corpus, including section f lore and j learned. Make use of the fact that nltk.

Do the same thing with the Lancaster Stemmer and see if you observe any differences. Process this list using a for loop, and store the length of each word in a new list lengths.

Then each time through the loop, use append to add another length value to the list. Now do the same thing using a list comprehension.

This happens to be the legitimate interpretation that bilingual English-Spanish speakers can assign to Chomsky's famous nonsense phrase, colorless green ideas sleep furiously according to Wikipedia.

Now write code to perform the following tasks:. Investigate this phenomenon with the help of a corpus and the findall method for searching tokenized text described in 3.

Define regular expressions to convert English words into corresponding lolspeak words. Implement this algorithm in Python. Use Punkt to perform sentence segmentation.

Extend the concordance search program in 3. For simplicity, work with a single character encoding and just a few languages. For each word, compute the WordNet similarity between all synsets of the word and all synsets of the words in its context.

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