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Ultimate Ears BOOM 3 and MEGABOOM 3 get tougher and cheaper
Ultimate Ears has revealed its new BOOM 3 and MEGABOOM 3 Bluetooth speakers, making them more rugged, longer-lasting, and more affordable than the old pair. It’s also taken the opportunity to give the two new speakers an aesthetic revamp, with the result being a cleaner and more modern-looking design.
Gone is the thick rubber bar running down the front of each speaker, though the sizable – and easily-squeezed – volume control buttons remain. The fabric, too, has been updated, with a tighter weave and a more iridescent look. It’s the same material as used for clothing for emergency services personnel, Ultimate Ears says, underscoring its ruggedness.
In fact, the resilience of both the BOOM 3 and MEGABOOM 3 is improved all-round. They’re now IP67 dust and waterproof, versus the IPX7 of the old BOOM 2, and they also float, too. A new hanging loop has been added to the back.
Even if you’re not hurling them into swimming pools, the other design tweaks make a lot more sense too. The charging port – still microUSB, since Ultimate Ears tells me it’s not convinced USB-C is mainstream enough yet – is now on the back, under a rubber flap, rather than on the bottom. It comes after owners of the old speakers said they’d prefer not to have to invert the whole thing in order to charge it.
A full charge lasts up to 16 hours on the BOOM 3, and up to 20 hours on the MEGABOOM 3. With the optional POWER UP charging dock, launched alongside the Ultimate Ears BLAST and MEGABLAST last year, you can top up either speaker by just placing it atop the base station. Charging takes around 2.75 hours.
On the sound side, MEGABOOM 3’s bigger passive radiators should make for a louder-sounding speaker with implements in the low-end. Having spent some time listening to the speaker myself, I can say it definitely pumps out more bass than you’d expect, given the 8.9-inch height. Happily that’s without sacrificing on treble, though.
Bluetooth range has been increased by 50-percent, now coming in at up to 150 feet. There’s a new app, too, which now consolidates Ultimate Ears’ BOOM and MEGABOOM models into one place. You still get all of the old features, including a custom equalizer, remote on/of, and PartyUp to sync up to 150 speakers together to play the same thing.
New, though, is the Magic Button on the top of the BOOM 3 and MEGABOOM 3. As with the old speakers, you can tap it once to toggle between play and pause, and double-tap it to skip forward a track. However, you can also long-press it to power up the speaker, connect to your smartphone, and start playing a preselected favorite playlist – either in Apple Music if you’re paired to an iPhone, or Deezer Premium if you’re using an Android device.
You can configure up to four playlists in the Ultimate Ears app, and then long-press again on the Magic Button to cycle between them. Ultimate Ears tells me it’s working on adding support for remote playlist access for other apps and streaming services.
As for colors, the BOOM 3 and MEGABOOM 3 will be offered in Night (Black), Sunset (Red), Lagoon (Blue), and Ultraviolet (Purple). Apple Stores will also have two exclusive finishes: Denim (Dark Blue) and Cloud (Light Blue). The BOOM 3 and MEGABOOM 3 will go on sale in the US, and select countries in Europe and Asia, in September. It’ll be priced at $149.99 for the BOOM 3 and $199.99 for the MEGABOOM 3 – each $50 less than their predecessors – while the POWER UP will be $39.99.
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Pixel 3 XL and Pixel 3 hands-on: The new Android flagships
Did you feign surprise when Google took the wraps off the Pixel 3 and Pixel 3 XL? The new Android smartphones had their surprise thoroughly spoiled by some of the most expansive and thorough leaks in the weeks and months running up to their debut today. Still, while the news may not have been entirely fresh, the fact remains that these are very important phones in the smartphone ecosystem.
Happily, it seems Google has pitched them. Both the Pixel 3 and Pixel 3 XL are bigger than their predecessors, with larger screens that dovetail nicely with the relentless bigger-is-better trend in mobile.
Of course, size wasn’t the biggest issue we had with last year’s Google Phones. The strange blue tint on the Pixel 2 XL’s screen proved to be a dealbreaker for many, and thankfully it’s been addressed for 2023. Indeed, the Pixel 3 XL’s screen is very pleasant indeed, with rich colors and a warmth its predecessor struggled to deliver, even after Google’s software tweaks.
The notch, cutting into the upper section of the screen and where Google hides the twin front cameras, is less intrusive than the leaked renders might have suggested, too. As per Apple and others, Google prefers wallpapers and UI elements that emphasize darker colors near the top of the screen, to help mask just how much is cut out. Nonetheless, just as I have with the iPhone XS Max, I suspect I’d quickly learn to ignore the Pixel 3 XL’s cut-out.
As for the Pixel 3, that’s pleasingly one-hand-friendly. Its display is crisp and bright – there doesn’t feel like there’s a “bad” screen to be had, this time around – and the body feels more premium than before. That’s in no small part down to the change in how Google uses its materials.
On the Pixel 2, the back panel was a combination of glass and metal: the former inset into a section of the latter, around the camera lens. The Pixel 3 and Pixel 3 XL, however, minimizes the metal to a smooth frame around the edges of each phone. The full back panel is now glass, which allows the $79 Pixel Stand to wirelessly charge it.
You still get the two-tone finish, courtesy of some etching, but it feels much more cohesive in your hand. Weight is up a little, and I can already tell the 2023 phones will be a little more slippery in my grip than last year’s, but I think that’s worth it for the more refined design. The contrasting power button remains a pleasing touch, and I wish Google would feature it on the black version of the phones, too.
Hardware, of course, is only half of the story. You’ll find other Android phones out there with Snapdragon 845 processors, more than the 128 GB of storage the Pixel 3 tops out at, and the same Qi charging and USB-C connectivity. What distinguishes Google’s phones, though, is the software.
On the one hand, you get the new improvements to the camera. A single 12.2-megapixel sensor on the rear, with f/1.8 aperture and optical image stabilization, but combined with Google’s Pixel Visual Core chip for new photo talents. That includes HDR+ and Top Shot, which grabs a sequence of frames and allows you to choose between them for the perfectly-posed image.
Super Res Zoom, meanwhile, combines multiple images to make a better-quality zoomed picture, despite the absence of an optical zoom lens. Night Sight, meanwhile, promises hugely more usable low-light shots, without having to resort to an LED flash. We’ll have to wait until we have a Pixel 3 and Pixel 3 XL in hand to actually test these new features out in the wild, but if they work as Google promises then rival phone-makers should be concerned. The Pixel 2 was still, many argued, the best phone camera around, despite being a year old, and the Pixel 3 only improves on it.
The other software improvements focus more on day to day use. There’s Android 9 Pie, of course, but the Pixel 3 debuts features like automatically transcribing spam calls without you having to listen in. It uses Google’s Duplex AI system, and you can guide the conversation with auto-suggestions.
We’re a long way from the days when Google’s smartphones were the budget option. With starting prices of $799 for the Pixel 3, and $899 for the Pixel 3 XL, these are going toe-to-toe with flagships from Apple, Samsung, and others. What may give Google the edge is just how capably it wields its machine learning and AI technologies.
Microsoft is rolling out new firmware updates for its Surface Pro 3 and Surface 3. While both tablets are picking up significant improvements, Surface 3 is the one getting the most fixes. In the January firmware update, the software giant is addressing a number of issues regarding Wi-Fi connectivity, sound, and UEFI BIOS. Additionally, the company is also releasing various fixes for the LTE version of Surface 3.
For Surface Pro 3, the software giant is also rolling out some important improvements. In the January firmware update for the tablet, Microsoft is improving the Surface Fingerprint Sensor for better accuracy, wireless controller, there are updated graphics and audio drivers, and the firmware also adds pen support in the UEFI menu.Surface 3
The January firmware will be listed as “System Firmware Update – 1/19/2023” when you view your update history in your Surface 3.
Surface System Aggregator Firmware update (v1.0.51500.0) improves reliability with the Surface 3 Type Cover.
Surface UEFI update (v1.51116.18.0) adds ability within Windows Power & sleep settings to turn off Wi-Fi during sleep. Improved touch support for UEFI menus and support for 3rd party onscreen keyboards improvements.
Microsoft Surface ACPI-Compliant Control Method Battery driver update (v220.127.116.11) ensures correct surface driver is installed.
Wireless Network Controller and Bluetooth driver update (v15.68.9037.59) improves access point compatibility and throughput on 5GHz.
Surface Digitizer Integration driver update (v18.104.22.168) improves pen pairing feature with newest surface pen.
Surface Pen Pairing driver update (v22.214.171.124) improves pen pairing feature with newest surface pen.
Audio Device driver update (v604.10135.7777.2109) improves audio quality with some applications.
I2S Audio Codec driver update (v6.2.9600.527) improves audio quality with some applications.
Serial IO GPIO Controller driver update (v604.10146.2652.3930) improves system stability and touch screen reliability.
Dynamic Platform & Thermal Framework Driver update (v604.10146.2651.1559) improves system stability and touch screen reliability.
Serial IO I2C ES Controller driver update (v604.10146.2654.3564) improves system stability and touch screen reliability.
Serial IO SPI Controller driver update (v604.10146.2657.947) improves system stability and touch screen reliability.
Serial IO UART Controller driver update (v604.10146.2653.391) improves system stability and touch screen reliability.
Sideband Fabric Device update (v604.10146.2655.573) improves system stability and touch screen reliability.
Trusted Execution Engine Interface driver update (v126.96.36.1997) improves system stability and touch screen reliability.
In addition to the updates list above, the following updates are available for Surface 3 (AT&T 4G LTE), Surface 3 (Verizon 4G LTE), Surface 3 (4G LTE) in North America (non-AT&T), Surface 3 (4G LTE) in Europe and Surface 3 (Y!mobile 4G LTE):
GNSS Bus Driver update (v20.23.8244.18) improves GPS experience.
GNSS 47531 Geolocation Sensor driver update (v20.23.8244.18) improves GPS experience.
Surface CoSAR driver update (v2.0.304.0) enhances the Wi-Fi connectivity reliability while mobile broadband is ON.
In addition to the updates list above, the following update is available for Surface 3 (Verizon 4G LTE):
Surface IA7260 Firmware Update (v1544.01.00.28) improves mobile broadband network stability.Surface Pro 3
The January firmware will be listed as “System Firmware Update – 1/19/2023” and “Intel Corporation driver update for Intel(R) HD Graphics Family” when you view your update history in your Surface Pro 3.
Surface Pro Embedded Controller Firmware update (v188.8.131.52) improves system start-up reliability.
Surface Pro UEFI update (v3.11.1150.0) adds pen support in UEFI menus and improved support for 3rd party onscreen keyboards.
Surface Fingerprint Sensor driver update (v184.108.40.206) improves accuracy.
Wireless Network Controller and Bluetooth driver update (v15.68.9037.59) improves access point compatibility and throughput on 5GHz.
HD Graphics Family driver update 4331 (v220.127.116.1131) improves color calibration and system stability.
Display Audio driver update (v6.16.00.3189) supports compatibility with the updated HD Graphics Family driver.
Surface Cover Audio driver update (v2.0.1220.0) improves system stability.
Microsoft Surface ACPI-Compliant Control Method Battery driver update (v18.104.22.168) ensures correct surface driver is installed.
Source Surface Update History
It’s October 4th, 2011, and Apple is hosting its highly anticipated iPhone event. SVP of Marketing Phil Schiller is on stage, and after about 5 minutes of discussing changes to the iPod line, he utters the words that everyone has been waiting to hear: “Next, iPhone.”
A sense of disappointment spread throughout the tech world as Schiller went on to unveil a familiar-looking iPhone 4S. Where was this teardrop-shaped iPhone 5 that we had been hearing so much about? With the bigger screen, and LTE? What about all of those leaked cases?
Of course, the 4S would go on to be a huge hit for Apple. But the whole experience has left a lot of consumers with low expectations for this year’s iPhone release. Well it’s time to raise them. There are actually a few reasons why you should be excited about Apple’s next handset…Pattern
So why does Apple do this? It could be related to carrier contracts. The average cell phone customer can get a subsidized handset every two years, so it could be that Apple has made this its timeline for major iPhone updates. Ie: it’s easier to buy a handset on a two-year contract if you’re not worried about it being completely obsolete in 12 months. Also, the decision could have something to do with Moore’s law, which says that the number of transistors on integrated circuits doubles every two years. How can Apple dramatically update the iPhone every year, if the technology inside is only updated every two?
Regardless of the reasoning behind it, the pattern is evident. And if Apple continues that pattern with this year’s release, the next iPhone should be a major update.Evidence
Other than a number of third-party cases, we really didn’t see a whole lot of physical evidence last year supporting the ‘iPhone 5’ theory. There were no radically different-looking leaked components, or display panels. Nothing. In fact, most of the parts we did see looked a lot like iPhone 4 parts. Go figure.
This year, however, that’s just not the case. We’ve actually seen a ton of evidence suggesting that the next iPhone will look different than the current model, from 4-inch display panels to engineering samples and schematics. And let’s not forget those two-tone back panels that keep popping up.
All of these components come from different sources, but all of them point to a similar design — an iPhone with a part-glass, part-aluminum back panel and a larger 4-inch display. Keep in mind, these are all likely prototype parts. But it’s still evidence that Apple is working on a new design for its next smartphone. Which, once again, suggests that this year’s iPhone will be a significant update.Pressure
To say that Apple is under a lot of pressure to deliver a hit smartphone this year is a massive understatement. This will be the company’s first handset since Steve Jobs passed. And even though it’s believed that he played a large part in its development, his absence will be on everyone’s minds. Can Apple deliver a hit product without its beloved visionary?
Also keep in mind that Apple sold 37 million handsets during the quarter following the iPhone 4S release last year. So to top that, which it’s expected to do, Apple has to sell in upwards of 40 million phones during the 2012 holiday season. That means that it has to essentially convince 40 million people that its new iPhone is better than the competitions’ handsets, which, by the way, are looking better than ever.
Samsung unveiled its latest flagship handset, the Galaxy S III, back in May of this year. And it’s already believed to have sold over 10 million of them. Factor in the new Android 4.1 JellyBean update, which has been getting rave reviews, and the expected onslaught of new Windows Phone 8 hardware this fall, and you can see that Apple needs to come up big with its next iPhone to maintain marketshare.Conclusion
All of these things combine for a pretty good argument on why we should expect a major update to Apple’s smartphone line this year. The pattern is there. The evidence is there. And the pressure?
The iPhone is Apple’s baby. It’s the company’s best-selling product, with the highest profit margins. And even though it’s sold extremely well in the past, as Nokia and RIM have proven, that can change in an instant. With the competition hotter this year than ever before, Apple doesn’t just want its next iPhone to be a hit, it needs it to be. What Tim Cook’s team unveils this October will set the tone for the rest of the CEO’s tenure, and the company’s immediate future.
Oh yeah, I’d say the pressure’s definitely there.
Linux is an incredibly powerful and versatile operating system that is widely used in computing industry. One of most important aspects of using any computer system is ability to manage files and directories. In this article, we will be discussing three ways to permanently and securely delete files and directories in Linux.Why Secure File Deletion is Important
When you delete a file or directory from your computer, it does not necessarily mean that data is gone forever. In most cases, data is still present on your hard drive or storage device, but it is marked as “free space” that can be overwritten by new data. This means that if someone were to gain access to your computer, they could potentially recover your deleted files and view sensitive information.
Secure file deletion is a way to ensure that your data is completely and permanently erased from your storage device. This can help protect your sensitive information from falling into wrong hands. In Linux, there are several ways to achieve this.Using ‘shred’ Command
The ‘shred’ command is a powerful tool that can be used to securely delete files and directories in Linux. This command overwrites data in file or directory multiple times, making it nearly impossible to recover original data.
To use ‘shred’ command, you will need to open a terminal window and navigate to location of file or directory you want to delete. Once you are in correct directory, you can use following command −shred -n 10 -z file.txt
In this example, we are deleting a file called ‘file.txt’. ‘-n 10’ option tells ‘shred’ command to overwrite data in file 10 times. ‘-z’ option tells ‘shred’ to add a final overwrite with zeros, which helps to hide fact that file was shredded.Using ‘wipe’ Command
The ‘wipe’ command is another tool that can be used to securely delete files and directories in Linux. This command works by overwriting data in file or directory with random data, making it nearly impossible to recover original data.
To use ‘wipe’ command, you will need to open a terminal window and navigate to location of file or directory you want to delete. Once you are in correct directory, you can use following command −wipe -rf directory/
In this example, we are deleting a directory called ‘directory/’. ‘-r’ option tells ‘wipe’ command to delete directory and its contents recursively. ‘-f’ option tells ‘wipe’ to force deletion, which means it will not prompt you for confirmation before deleting files.Using ‘dd’ Command
The ‘dd’ command is a versatile tool that can be used for a variety of tasks, including securely deleting files and directories in Linux. This command works by overwriting data in file or directory with zeros, making it nearly impossible to recover original data.
To use ‘dd’ command, you will need to open a terminal window and navigate to location of file or directory you want to delete. Once you are in correct directory, you can use following command −dd if=/dev/zero of=file.txt bs=1M count=10
In this example, we are deleting a file called ‘file.txt’. ‘if=/dev/zero’ option tells ‘dd’ command to read zeros from ‘zero’ device, which is a special file that generates an endless stream of zeros. ‘of=file.txt’ option tells ‘dd’ to write zeros to file we want to delete.
The ‘bs=1M’ option tells ‘dd’ to use a block size of 1 megabyte, which speeds up process. ‘count=10’ option tells ‘dd’ to write 10 blocks of data to file, which effectively overwrites data in file.Tips for Secure File Deletion in Linux
Always double-check file or directory you are deleting before using any of these commands. Once data is deleted, it cannot be recovered.
Make sure you have necessary permissions to delete file or directory. If you do not have necessary permissions, you may need to use ‘sudo’ command to run commands as root user.
Use a combination of these methods to ensure maximum security. For example, you could use ‘shred’ command to securely delete sensitive files and ‘wipe’ command to securely delete directories.
Remember that these commands are irreversible. Once data is deleted, it cannot be recovered.Conclusion
Secure file deletion is an important aspect of computer security, and Linux provides several tools to achieve this. ‘shred’, ‘wipe’, and ‘dd’ commands are powerful tools that can be used to securely delete files and directories in Linux. By following tips outlined in this article, you can ensure that your sensitive information is permanently and securely deleted from your computer.
This article was published as a part of the Data Science BlogathonIntroduction
This article is part of an ongoing blog series on Natural Language Processing (NLP). In part-1and part-2 of this blog series, we complete the theoretical concepts related to NLP. Now, in continuation of that part, in this article, we will cover some of the new concepts.
In this article, we will understand the terminologies required and then we start our journey towards text cleaning and preprocessing, which is a very crucial component while we are working with NLP tasks.
This is part-3 of the blog series on the Step by Step Guide to Natural Language Processing.
Table of Contents
1. Familiar with Terminologies
2. What is Tokenization?
Regular Expression Tokenization
Sentence and Word Tokenization
3. Noise Entities Removal
Removal of Punctuation marks
Removal of stopwords, etc.
4. Data Visualization for Text Data
5. Parts of Speech (POS) TaggingFamiliar with Terminologies
Before moving further in this blog series, I would like to discuss the terminologies that are used in the series so that you have no confusion related to terminologies:Corpus
A Corpus is defined as a collection of text documents.
A data set containing news is a corpus or
The tweets containing Twitter data are a corpus.
So corpus consists of documents, documents comprise paragraphs, paragraphs comprise sentences and sentences comprise further smaller units which are called Tokens.
Tokens can be words, phrases, or Engrams, and Engrams are defined as the group of n words together.
For example, consider the sentence given below:Sentence: I like my iphone
For the above sentence, the different engrams are as follows:Uni-grams(n=1) are: I, like, my, iphone Di-grams(n=2) are: I like, like my, my iphone Tri-grams(n=3) are: I like my, like my iphone
So, uni-grams are representing one word, di-grams are representing two words together and tri-grams are representing three words together.Tokenization
It is the process of converting a text into tokens.Text object
The text object is a sentence or a phrase or a word or an article.Morpheme
In the field of NLP, a Morpheme is defined as the base form of a word. A token is generally made up of two components,
Morphemes: The base form of the word, and
Inflectional forms: The suffixes and prefixes added to morphemes.
Let’s discuss the structure of the tokens:
For Example,Consider the word: Antinationalist
which is made up of the following components:
Inflectional forms- Anti and ist
It represents all the words and phrases used in a particular language or subject.What is Tokenization?
Tokenization is a process of splitting a text object into smaller units which are also called tokens. Examples of tokens include words, numbers, engrams, or even symbols. The most commonly used tokenization process is White-space Tokenization.
Let’s discuss the two different types of Tokenization:
Regular Expression TokenizationWhite-space Tokenization
It is also known as unigram tokenization. In this process, we split the entire text into words by splitting them from white spaces.
For Example, Consider the following sentence-Sentence: I went to New-York to play football Tokens generated are: “I”, “went”, “to”, “New-York”, “to”, “play”, “football”
Notice that “New-York” is not split further because the tokenization process was based on whitespaces only.Regular Expression Tokenization
It is another type of Tokenization process, in which a regular expression pattern is used to get the tokens.
For Example, consider the following string containing multiple delimiters such as comma, semi-colon, and white space.Sentence:= “Basketball, Hockey; Golf Tennis" re.split(r’[;,s]’, Sentence Tokens generated are: “Basketball”, ”Hockey”, “Golf”, “Tennis”
Therefore, using Regular expression, we can split the text by passing a splitting pattern.NOTE
Tokenization can be performed at the sentence level or at the word level or even at the character level. Based on it we discuss the following two types of Tokenization:
Word TokenizationSentence Tokenization
Sentence tokenization, also known as Sentence Segmentation is the technique of dividing a string of written language into its component sentences. The idea here looks very simple. In English and some other languages, we can split apart the sentences whenever we see a punctuation mark.
However, even in English, this problem is not trivial due to the use of full stop characters for abbreviations. When processing plain text, tables of abbreviations that contain periods can help us to prevent incorrect assignment of sentence boundaries. In many cases, we use libraries to do that job for us, so don’t worry too much about the details for now.
In simple words, the Sentence tokenizer breaks text paragraphs into sentences.
Word tokenization, also known as Word Segmentation is the problem of dividing a string of written language into its component words. White space is a good approximation of a word divider in English and many other languages with the help of some form of Latin alphabet.
However, we still can have problems if we only split by space to achieve the wanted results. Some English compound nouns are variably written and sometimes they contain a space. In most cases, we use a library to achieve the wanted results, so again don’t worry too much about the details.
In simple words, Word tokenizer breaks text paragraphs into words.
Noise Entities Removal
Noise is considered as that piece of text which is not relevant to the context of the data and the final output.
Language stopwords (commonly used words of a language – is, am, the, of, in, etc),
URLs or links,
Social media entities (mentions, hashtags),
Punctuations, and Industry-Specific words.
The general steps which we have to follow to deal with noise removal are as follows:
Firstly, prepare a dictionary of noisy entities,
Then, iterate the text object by tokens (or by words),
Finally, eliminating those tokens which are present in the noise dictionary.Removal of Stopwords
Image Source: Google ImagesLet’s first understand what exactly are “Stopwords”?
Stop words are words that are separated out before or after the text preprocessing stage, as when we applying machine learning to textual data, these words can add a lot of noise. That’s why we remove these irrelevant words from our analysis. Stopwords are considered as the noise in the text.
Stop words usually refer to the most common words such as “and”, “the”, “a” in a language.
Note that there is no single universal list of stopwords. The list of the stop words can change depending on your problem statement.
For stopwords, we have an NLTK toolkit that has a predefined list of stopwords that refers to the most common words. If you use it for the first time, you need to download the stop words using this code:nltk. download(“stopwords”)
After completion of downloading, you can load the package of stopwords from the nltk. corpus and use it to load the stop words.
Now, let’s try to print the List of Stopwords in the English language:
After running the above code, we get the output as:
We would not want these words taking up space in our database, or taking up the valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to stop words. NLTK in python has a list of stopwords stored in 16 different languages, which means you can also work with other languages also.
You can see that the words “is” and “my” have been removed from the sentence.Homework Problem
Here, we will discuss only one technique of noise removal, but you can try some other techniques also by yourself which we discussed in the above examples. We will discuss all those topics while we implement the NLP project.
Data Visualization for Text Data
To visualize text data, generally, we use the word cloud but there are some other techniques also, which we can try to visualize the data such as,
Let’s discuss the word cloud in a more detailed manner:Word Cloud
Word Cloud is a data visualization technique in which words from a given text display on the main chart. Some properties associated with this chart are as follows:
In this technique, more frequent or essential words display in a larger and bolder font,
While less frequent or essential words display in smaller or thinner fonts.
This data visualization technique gives us a glance at what text should be analyzed, so it is a very beneficial technique in NLP tasks.
For more information, check the given documentation: WordCloud
Here, to draw the word cloud, we use the following text:
Code to print the word cloud:
A word cloud formed is shown below:
Conclusions inferred from the above word cloud:
The most frequent words display in larger fonts.
The word cloud can be displayed in any shape or image depend on input parameters.Homework Problem
You have to make the above word cloud in the form of a circle.Advantages of Word Cloud
They are fast.
They are engaging.
They are simple to understand.
They are casual and visually appealing.
They are non-perfect for non-clean data.
They lack the context of words.Parts of Speech (POS) Tagging
If we think about building a system where we can encode all this knowledge, then it may look like a very easy task, but for many decades, coding this knowledge into a machine learning model was a very hard NLP problem. POS tagging algorithms can predict the POS of the given word with a higher degree of precision.
Part of speech tags is the properties of words that define their main context, their function, and their usage in a sentence. It is defined by the relations of words with the other words in the sentence.
In this, our aim is to guess the part of speech for each token— whether it is a noun, a verb, an adjective, and so on. After knowing about the role of each word in the sentence, we will start to find out what the sentence is talking about.
Image Source: Google Images
Some of the commonly used parts of speech tags are:
Nouns, which define any object or entity
Verbs, which define some action and
Adjectives or Adverbs, which act as modifiers, quantifiers, or intensifiers in any sentence.
In a sentence, every word will be associated with a proper part of the speech tag.
For Example, Let’s consider the following sentence:Sentence: David has purchased a new laptop from the Apple store
In the below sentence, every word is associated with a part of the speech tag which defines its functions.
In this case, the associated POS tags with the above sentence are as follows:
“David’ has an NNP tag which means it is a proper noun,
“has” and “purchased” belongs to verb indicating that they are the actions and
“laptop” and “Apple store” are the nouns,
“new” is the adjective whose role is to modify the context of the laptop.
Machine learning models or rule-based models are applied to obtain the part of speech tags of a word. The most commonly used part of speech tagging notations is provided by the Penn Part of Speech Tagging.
You can get the POS of individual words as a tuple.
Why do we need Part of Speech (POS)?
Let’s discuss the need for POS tags with the following example:Sentence: Can you help me with the can?
Parts of speech (POS) tagging are crucial for syntactic and semantic analysis.
Therefore, for something like
In the sentence above, the word “can” has several semantic meanings.
The first “can” is used for question formation.
The second “can” at the end of the sentence is used to represent a container.
The first “can” is a verb, and the second “can” is a noun.
Therefore, giving the word a specific meaning allows the program to handle it correctly in both semantic and syntactic analysis.Applications of POS Tagging in NLP
Part of Speech tagging is used for many important purposes in NLP:Word Sense Disambiguation
Some language words have multiple meanings according to their usage.
For Example, Consider the two sentences given below:Please book my flight for Jodhpur I am going to read this book in the flight
In the above sentences, the word “Book” is used in different contexts, however, the part of speech tag for both of the cases are different.
In the first sentence, the word “book” is used as a verb, while in the second sentence it is used as a noun. (For similar purposes, we can also use Lesk Algorithm)Improving Word-based Features
A learning model could learn different contexts of a word when used word as the features, however, if the part of speech tag is linked with them, the context is preserved, thus making strong features.
For Example: Consider the following sentence:Sentence: "book my flight, I will read this book”
The tokens generated from the above text are as follows:(“book”, 2), (“my”, 1), (“flight”, 1), (“I”, 1), (“will”, 1), (“read”, 1), (“this”, 1)
The tokens generated from the above text with POS are as follows:(“book_VB”, 1), (“my_PRP$”, 1), (“flight_NN”, 1), (“I_PRP”, 1), (“will_MD”, 1), (“read_VB”, 1), (“this_DT”, 1), (“book_NN”, 1) Normalization and Lemmatization
POS tags are the basis of the lemmatization process for converting a word to its base form (lemma).Efficient Stopword Removal
POS tags are also useful in the efficient removal of stopwords.
For Example, there are some tags that always define the low frequency / less important words of a language. For example: (IN – “within”, “upon”, “except”), (CD – “one”,” two”, “hundred”), (MD – “may”, “must” etc)This ends our Part-3 of the Blog Series on Natural Language Processing!
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Please feel free to contact me on Linkedin, Email.About the Author Chirag Goyal
Currently, I am pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence.
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