Trending December 2023 # Duckduckgo Offers Its Take On A Natural Language Ai Integration With Duckassist # Suggested January 2024 # Top 17 Popular

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DuckDuckGo is out today with an update that brings a beta of a new feature called DuckAssist. The approach to AI is different from what we’ve seen from OpenAI’s ChatGPT and Microsoft’s broad focus. DuckAssist was designed with the goal of offering “more directly responsive” answers to questions summarized from Wikipedia and similar sources.

DuckDuckGo founder and CEO Gabriel Weinberg shared the news in a blog post this morning.

Generative artificial intelligence is hitting the world of search and browsing in a big way. At DuckDuckGo, we’ve been trying to understand the difference between what it could do well in the future and what it can do well right now. But no matter how we decide to use this new technology, we want it to add clear value to our private search and browsing experience.

With that in mind, DuckDuckGo has launched the first version of DuckAssist in beta for all users. As shown above, the “Instant Answer” feature may show up for certain searches at the top with an “Ask” button to get the natural AI response.

DuckAssist uses technology from both OpenAI and Anthropic and will summarize answers from Wikipedia “and occasionally related sites like Britannica — using DuckDuckGo’s active indexing.”

Weinberg highlights that DuckAssist is private, there’s no sign-up required, and it’s available now.

DuckDuckGo is planning to launch more AI-assisted features “in the coming months.” And if the DuckAssist beta goes well, it will officially be added to the browser “in the coming weeks.”

What should you expect from DuckAssist?

For the beta, DuckAssist is designed to be best for answering more objective, straightforward questions.

For this initial trial, DuckAssist is most likely to appear in our search results when users search for questions that have straightforward answers in Wikipedia. Think questions like “what is a search engine index?” rather than more subjective questions like “what is the best search engine?”. We are using the most recent full Wikipedia download available, which is at most a few weeks old. This means DuckAssist will not appear for questions more recent than that, at least for the time being. For those questions, our existing search results page does a better job of surfacing helpful information.

Weinberg also notes that you may not see DuckAssist “on many of our searches yet.” But with the combination of Wikipedia and generative AI, the feature can “vastly increase the number of Instant Answers we can provide.”

Here are some tips if you want to try it out:

Phrasing your search query as a question makes DuckAssist more likely to appear in search results.

If you’re fairly confident that Wikipedia has the answer to your query, adding the word “wiki” to your search also makes DuckAssist more likely to appear in search results.

For now, the DuckAssist beta is only available in English in our browsing apps (iOS, Android, and Mac) and browser extensions (Firefox, Chrome, and Safari). If the trial goes well, we plan to roll it out to all DuckDuckGo search users soon.

If you don’t want DuckAssist to appear in search results, you can disable “Instant Answers” in search settings. (Note: this will disable all Instant Answers, not just DuckAssist.)

Check out the full details in DuckDuckGo’s announcement post.

FTC: We use income earning auto affiliate links. More.

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What Is Chunking In Natural Language Processing?

This article was published as a part of the Data Science Blogathon

Dear readers,

In this blog, I will be discussing chunking both theoretically and practically in Python.

So, let’s begin…

NOTE: For the implementation, its better to use the Python IDLE as the output is a drawing of a tree which pops up in a separate window.


What is chunking?

Where is chunking used?

Types of chunking

Implementation of chunking in Python


What is chunking?

Chunking is defined as the process of natural language processing used to identify parts of speech and short phrases present in a given sentence.

For example, chunking can be done to identify and thus group noun phrases or nouns alone, adjectives or adjective phrases, and so on. Consider the sentence below:

“I had burgers and pastries for breakfast.”

In this case, if we wish to group or chunk noun phrases, we will get “burgers”, “pastries” and “lunch” which are the nouns or noun groups of the sentence.

Where is chunking used?

Why would we want to learn something without knowing where it is widely used?! Looking at the applications discussed in this section of the blog will help you stay curious till the end!

Chunking is used to get the required phrases from a given sentence. However, POS tagging can be used only to spot the parts of speech that every word of the sentence belongs to.

Interestingly, this process of chunking in NLP is extended to various other applications; for instance, to group fruits of a specific category, say, fruits rich in proteins as a group, fruits rich in vitamins as another group, and so on. Besides, chunking can also be used to group similar cars, say, cars supporting auto-gear into one group and the others which support manual gear into another chunk and so on.

Types of Chunking

There are, broadly, two types of chunking:

Chunking up

Chunking down

Chunking up:

Here, we don’t dive deep; instead, we are happy with just an overview of the information. It just helps us get a brief idea of the given data.

Chunking down:

Unlike the previous type of chunking, chunking down helps us get detailed information.

So, if you just want an insight, consider “chunking up” otherwise prefer “chunking down”.

Implementation of chunking in Python

Imagine a situation in which you want to extract all the verbs from the given text for your analysis. Thus, in this case, we must consider the chunking of verb phrases. This is because our objective is to extract all verb phrases from the given piece of text. Chunking is done with the help of regular expressions.

Don’t worry if it’s the first time you are coming across the term, “regular expressions”. The below table is here, at your rescue:





The preceding character can occur zero or more times meaning that the preceding character may or may not be there.

ab* matches all inputs starting with ab and then followed by zero or more number of b’s. The pattern will match ab, abb ,abbb and so on.


The preceding character should occur at least once.

a+ matches a,aa,aaa and so on.


The preceding character may not occur at all or occur only once meaning the preceding character is optional.

ab? matches ab,abb but not abbb and so on.

The above table includes the most common regular expressions used. Regular expressions are very useful in the command line especially while deleting, locating, renaming, or moving files.

Anyways, for this implementation, we will only be using *. Feel free to look at the above table to familiarize yourself with the symbol!

We will be performing chunking using nltk, the most popular NLP library. So, let us first import it.

import nltk

Let’s consider the below sample text which I created on my own. Feel free to replace the below with any sample text you like to implement chunking!

sample_text=""" Rama killed Ravana to save Sita from chúng tôi legend of the Ramayan is the most popular Indian epic.A lot of movies and serials have already been shot in several languages here in India based on the Ramayana. """

Clearly, the data has to be sentence tokenized and then word tokenized before we proceed. Tokenization is nothing but the process of breaking down the given piece of text into smaller units such as sentences, in the case of sentence tokenization and words, in the case of word tokenization.

Followed by tokenization, POS(part-of-speech) tagging is done for each word, in which the part-of-speech of every word will be identified. Now, we are interested only in the verb part-of-speech and wish to extract the same.

Hence, specify the part-of-speech of our interest using the required regular expression as follows:

VB: {}

tokenized=nltk.sent_tokenize(sample_text) for i in tokenized: words=nltk.word_tokenize(i) # print(words) tagged_words=nltk.pos_tag(words) # print(tagged_words) chunkGram=r"""VB: {}""" chunkParser=nltk.RegexpParser(chunkGram) chunked=chunkParser.parse(tagged_words) chunked.draw()

The regular expression(RE) is enclosed within angular brackets() which in turn are enclosed within curly brackets({ and }).

NOTE: Specify the RE according to the required POS

VB stands for the verb POS. The dot succeeding the VB means to match any character following VB. The question mark after the dot specifies that any character after B must occur only once or must not occur at all. However, from the table which we saw previously, this character is optional. We have framed the regular expression in this manner because, in NLTK, verb phrases include the following POS tags:




Verb in its base form


verb in its past tense


verb in its present tense


verb in its past participle form


Verb in its present tense but not in third person singular


Verb in its present tense and is third person singular

Thus, verb phrases can belong to any of the above POS. That’s why the regular expression is framed as VB.? which includes all of the above categories. RegexpParser package is used to check if a POS satisfies our required pattern which we have mentioned using the RE previously.

The entire code can be seen as follows:

import nltk'averaged_perceptron_tagger') sample_text=""" Rama killed Ravana to save Sita from chúng tôi legend of the Ramayan is the most popular Indian epic.A lot of movies and serials have already been shot in several languages here in India based on the Ramayana. """ tokenized=nltk.sent_tokenize(sample_text) for i in tokenized: words=nltk.word_tokenize(i) # print(words) tagged_words=nltk.pos_tag(words) # print(tagged_words) chunkGram=r"""VB: {}""" chunkParser=nltk.RegexpParser(chunkGram) chunked=chunkParser.parse(tagged_words) chunked.draw() Results

Finally, we obtain a tree form of the POS of the words along with the words whose POS matches with the given RE. The snapshot of the output obtained for the sample text passed by us can be seen in the above figures.

Observe that the words which satisfy our RE for verb phrases alone are clearly highlighted in the output. Hence, chunking of verb phrases has been performed successfully.

Hope you found my article useful.

Thank You!


1. Implementing chunking in Python

2. Theory behind chunking

3. Full list of POS available in NLP

About Me

I am Nithyashree V, a final year BTech Computer Science and Engineering student. I love learning such cool technologies and putting them into practice, especially observing how they help us solve society’s challenging problems. My areas of interest include Artificial Intelligence, Data Science, and Natural Language Processing.

Here is my LinkedIn profile: My LinkedIn

You can read my other articles on Analytics Vidhya from here.

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Natural Language Processing Step By Step Guide

This article was published as a part of the Data Science Blogathon


Basic understanding of Natural Language Processing.

Learn Various Techniques used for the implementation of NLP.

Understand how to use NLP for text mining.


You must have a basic knowledge of Python.

As we know every piece of data has some meaning in its position. Most important is that text data is getting generated in various formats like reviews, SMS, emails, and many more for every moment. The main purpose of this article is to understand the basic idea of NLP and how it will impact our day-to-day life. So let’s go.


NLP stands for Natural Language Processing, a part of Computer Science, Human Language, and Artificial Intelligence. This technology is used by computers to understand, analyze, manipulate, and interpret human languages.

NLP algorithms are widely used everywhere in areas like Gmail spam, any search, games, and many more.

Why NLP is so important?

Text data in a massive amount:

NLP helps machines to interact with humans in their language and perform related tasks like reading text, understand speech and interpret it in well format. Nowadays machines can analyze more data rather than humans efficiently. All of us know that every day plenty amount of data is generated from various fields such as the medical and pharma industry, social media like Facebook, Instagram, etc. And this data is not well structured (i.e. unstructured) so it becomes a tedious job, that’s why we need NLP.

Unstructured data to structured:

We know that supervised and unsupervised learning and deep learning are now extensively used to manipulate human language. That’s why we need a proper understanding of the text. I am going to explain this understanding in this chúng tôi is very important to get exact or useful insights from text. Meaningful information is gathered

Components of NLP

NLP is divided into two components.

Natural Language Understanding

Natural Language Generation

Components of NLP

Natural Language Understanding:-

Natural Language Understanding (NLU) helps the machine to understand and analyze human language by extracting the text from large data such as keywords, emotions, relations, and semantics, etc.

Let’s see what challenges are faced by a machine-

For Example:-

He is looking for a match.

What do you understand by the ‘match’ keyword? Does it partner or cricket or football or anything else?

This is Lexical Ambiguity. It happens when a word has different meanings. Lexical ambiguity can be resolved by using parts-of-speech (POS)tagging techniques.

The Fish is ready to eat.

What do you understand by the above example? Is the fish ready to eat his/her food or fish is ready for someone to eat? Got confused!! Right? We will see it practically below.

This is Syntactical Ambiguity which means when we see more meanings in a sequence of words and also Called Grammatical Ambiguity.

Natural Language Generation:-

It is the process of extracting meaningful insights as phrases and sentences in the form of natural language.

It consists −

Text planning − It includes retrieving the relevant data from the domain.

Sentence planning − It is nothing but a selection of important words, meaningful phrases, or sentences.

Phases of NLP

Phases of NLP

-Lexical Analysis:

It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in that particular language. The lexical analysis divides the text into paragraphs, sentences, and words. So we need to perform Lexicon Normalization.

The most common lexicon normalization techniques are Stemming:

Stemming: Stemming is the process of reducing derived words to their word stem, base, or root form—generally a written word form like-“ing”, “ly”, “es”, “s”, etc

Lemmatization: Lemmatization is the process of reducing a group of words into their lemma or dictionary form. It takes into account things like POS(Parts of Speech), the meaning of the word in the sentence, the meaning of the word in the nearby sentences, etc. before reducing the word to its lemma.

-Syntactic Analysis:

Syntactic Analysis is used to check grammar, arrangements of words, and the interrelationship between the words.

Example: Mumbai goes to the Sara

Here “Mumbai goes to Sara”, which does not make any sense, so this sentence is rejected by the Syntactic analyzer.

Syntactical parsing involves the analysis of words in the sentence for grammar. Dependency Grammar and Part of Speech (POS)tags are the important attributes of text syntactic.

-Semantic Analysis:

Retrieves the possible meanings of a sentence that is clear and semantically correct. Its process of retrieving meaningful insights from text.

–Discourse Integration:

It is nothing but a sense of context. That is sentence or word depends upon that sentences or words. It’s like the use of proper nouns/pronouns.

For example, Ram wants it.

In the above statement, we can clearly see that the “it” keyword does not make any sense. In fact, it is referring to anything that we don’t know. That is nothing but this “it” word depends upon the previous sentence which is not given. So once we get to know about “it”, we can easily find out the reference.

–Pragmatic Analysis:

It means the study of meanings in a given language. Process of extraction of insights from the text. It includes the repetition of words, who said to whom? etc.

It understands that how people communicate with each other, in which context they are talking and so many aspects.

Okay! .. So at this point, we came to know that all the basic concepts of NLP.

Here we will discuss all these points practically …so let’s move on!

Implementation of NLP using Python

I am going to show you how to perform NLP using Python. Python is very simple, easy to understand and interpret.

First, we will import all necessary libraries as shown below:

# Importing the libraries import pandas as pd import re from nltk.corpus import stopwords from nltk.stem.porter import PorterStemmer

In the above code, we have imported libraries such as pandas to deal with data frames/datasets, re for regular expression, nltk is a natural language tool kit in which we have imported modules like stopwords which is nothing but “dictionary” and PorterStemmer to generate root word.

df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv',header=0,index_col=0) df.head() # Null Entries df.isna().sum()

Here we have read the file named “Women’s Clothing E-Commerce Reviews” in CSV(comma-separated value) format. And also checked for null values.

You can find this dataset on this link:

import matplotlib.pyplot as plt import seaborn as sns sns.countplot(x='Rating',data=df_temp) plt.title("Distribution of Rating")

Further, we will perform some data visualizations using matplotlib and seaborn libraries which are really the best visualization libraries in Python. I have taken only one graph, you can perform more graphs to see how your data is!'stopwords') stops=stopwords.words("english")

From nltk library, we have to download stopwords for text cleaning.

review=df_temp[['Review','Recommended']] pd.DataFrame(review) def tokens(words): words = re.sub("[^a-zA-Z]"," ", words) text = words.lower().split() return " ".join(text) review['Review_clear'] = review['Review'].apply(tokens) review.head() corpus=[] for i in range(0,22628): Review=re.sub("[^a-zA-Z]"," ", df_temp["Review"][i]) Review=Review.lower() Review=Review.split() ps=PorterStemmer() Review=[ps.stem(word) for word in Review if not word in set(stops)] tocken=" ".join(Review) corpus.append(tocken)

Here we will perform all operations of data cleaning such as lemmatization, stemming, etc to get pure data.

positive_words =[] for i in positive.Review_clear: positive_words.append(i) positive_words = ' '.join(positive_words) positive_words

Now it’s time to see how many positive words are there in “Reviews” from the dataset by using the above code.

negative_words = [] for j in Negative.Review_clear: negative_words.append(j) negative_words = ' '.join(negative_words) negative_words

Now it’s time to see how many negative words are there in “Reviews” from the dataset by using the above code.

# Library for WordCloud from wordcloud import WordCloud import matplotlib.pyplot as plt wordcloud = WordCloud(background_color="white", max_words=len(negative_words)) wordcloud.generate(positive_words) plt.figure(figsize=(13,13)) plt.imshow(wordcloud, interpolation="bilinear") plt.axis("off")

By using the above code, we can simply show the word cloud of the most common words in the Reviews column in the dataset.

So, Finally, we have done all concepts with theory and implementation of NLP in Python…..!

Advantages of NLP

Removes unnecessary information.

NLP helps computers to interact with humans in their languages

NLP may not show full context.

NLP is unpredictable sometimes.

Everyday NLP examples

There are many common day-to-day life applications of NLP. Apart from virtual assistants like Alexa or Siri, here are a few more examples you can see.

Email filtering. Spam messages whose content is malicious get automatically filtered by the Gmail system and put into the spam folder.

Autocorrection of any text by using techniques of NLP. Sometimes we see that in mobile chat application or google search our word/sentence get automatically autocorrected. This is because of NLP.


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Basics Of Natural Language Processing(Nlp) For Absolute Beginners


According to industry estimates, only 21% of the available data is present in a structured form. Data is being generated as we speak, as we tweet, as we send messages on WhatsApp and in various other activities. The majority of this data exists in the textual form, which is highly unstructured in nature. 

Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system. In order to produce significant and actionable insights from text data, it is important to get acquainted with the basics of Natural Language Processing (NLP).

Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.

In this article, we will talk about the basics of different techniques related to Natural Language Processing.

Table of Contents

What are Corpus, Tokens, and Engrams?

What is Tokenization?

What is White-space Tokenization?

What is Regular Expression Tokenization?

What is Normalization?

What is Stemming?

What is Lemmatization?

Part of Speech tags in NLP

Grammar in NLP and its types

What is Constituency Grammar?

What is Dependency Grammar?

Let’s Begin!

What are Corpus, Tokens, and Engrams?

A Corpus is defined as a collection of text documents for example a data set containing news is a corpus or the tweets containing Twitter data is 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 this given sentence-

“I love my phone.”

In this sentence, the uni-grams(n=1) are: I, love, my, phone

Di-grams(n=2) are: I love, love my, my phone

And tri-grams(n=3) are: I love my, love my phone

So, uni-grams are representing one word, di-grams are representing two words together and tri-grams are representing three words together.

2. What is Tokenization?

Let’s discuss Tokenization now. Tokenization is a process of splitting a text object into smaller units which are also called tokens. Examples of tokens can be words, numbers, engrams, or even symbols. The most commonly used tokenization process is White-space Tokenization.

2.1 What is White-space Tokenization?

Also known as unigram tokenization. In this process, the entire text is split into words by splitting them from white spaces.

For example, in a sentence- “I went to New-York to play football.”

This will be splitted into following tokens: “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.

2.2 What is Regular Expression Tokenization?

The other type of tokenization process is Regular Expression Tokenization, 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= “Football, Cricket; Golf Tennis" re.split(r’[;,s]’, Sentence

Tokens= “Football”, ”Cricket”, “Golf”, “Tennis”

Using Regular expression, we can split the text by passing a splitting pattern.

Tokenization can be performed at the sentence level or at the world level or even at the character level.

3. What is Normalization?

The next technique is Normalization. In the field of linguistics and NLP, a Morpheme is defined as the base form of a word. A token is generally made up of two components, Morphemes, which are the base form of the word, and Inflectional forms, which are essentially the suffixes and prefixes added to morphemes.

For example, consider the word Antinationalist,

which is made up of Anti and ist as the inflectional forms and national as the morpheme. Normalization is the process of converting a token into its base form. In the normalization process, the inflection from a word is removed so that the base form can be obtained. So, the normalized form of anti-nationalist is national.

Normalization is useful in reducing the number of unique tokens present in the text, removing the variations of a word in the text, and removing redundant information too. Popular methods which are used for normalization are Stemming and Lemmatization.

Let’s discuss them in detail!

3.1 What is Stemming?

Stemming is an elementary rule-based process for removing inflectional forms from a token and the outputs are the stem of the world.

For example, “laughing”, “laughed“, “laughs”, “laugh” will all become “laugh”, which is their stem, because their inflection form will be removed.

Stemming is not a good normalization process because sometimes stemming can produce words that are not in the dictionary. For example, consider a sentence: “His teams are not winning”

After stemming the tokens that we will get are- “hi”, “team”, “are”, “not”,  “winn”

Notice that the keyword “winn” is not a regular word and “hi” changed the context of the entire sentence.

Another example could be-

3.2 What is Lemmatization?

Lemmatization, on the other hand, is a systematic step-by-step process for removing inflection forms of a word. It makes use of vocabulary, word structure, part of speech tags, and grammar relations.

The output of lemmatization is the root word called a lemma. For example,

Also, since it is a systematic process while performing lemmatization one can specify the part of the speech tag for the desired term and lemmatization will only be performed if the given word has the proper part of the speech tag. For example, if we try to lemmatize the word running as a verb, it will be converted to run. But if we try to lemmatize the same word running as a noun it won’t be converted.

A detailed explanation of how Lemmatization works by the step-by-step process to remove inflection forms of a word-

Let us now look at some of the syntax and structure-related properties of text objects. We will be talking about the part of speech tags and grammar.

4. Part of Speech(PoS) Tags in Natural Language Processing-

Part of speech tags or PoS tags is the properties of words that define their main context, their function, and the usage in a sentence. 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 the 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,

“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 their functions.

In this case “David’ has 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 laptop.

Part of speech tags is defined by the relations of words with the other words in the sentence. 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.

Part of speech tags have a large number of applications and they are used in a variety of tasks such as text cleaning, feature engineering tasks, and word sense disambiguation. For example, consider these two sentences-

Sentence 1:  “Please book my flight for NewYork”

Sentence 2: “I like to read a book on NewYork”

In both sentences, the keyword “book” is used but in sentence one, it is used as a verb while in sentence two it is used as a noun.

5. Grammar in NLP and its types-

Now, let’s discuss grammar. Grammar refers to the rules for forming well-structured sentences. The first type of Grammar is Constituency grammar.

5.1 What is Constituency Grammar?

Any word, group of words, or phrases can be termed as Constituents and the goal of constituency grammar is to organize any sentence into its constituents using their properties. These properties are generally driven by their part of speech tags, noun or verb phrase identification.

For example, constituency grammar can define that any sentence can be organized into three constituents- a subject, a context, and an object.

These constituents can take different values and accordingly can generate different sentences. For example, we have the following constituents-

Some of the examples of the sentences that can be generated using these constituents are-

“The dogs are barking in the park.”

“They are eating happily.”

“The cats are running since morning.”

Another view to look at constituency grammar is to define their grammar in terms of their part of speech tags. Say a grammar structure containing a [determiner, noun] [ adjective, verb] [preposition, determiner, noun] which corresponds to the same sentence- “The dogs are barking in the park.”

5.2 What is Dependency Grammar?

A different type of grammar is Dependency Grammar which states that words of a sentence are dependent upon other words of the sentence. For example, in the previous sentence “barking dog” was mentioned and the dog was modified by barking as the dependency adjective modifier exists between the two.

Dependency grammar organizes the words of a sentence according to their dependencies. One of the words in a sentence acts as a root and all the other words are directly or indirectly linked to the root using their dependencies. These dependencies represent relationships among the words in a sentence and dependency grammars are used to infer the structure and semantics dependencies between the words.

Let’s consider an example. Consider the sentence:

“Analytics Vidhya is the largest community of data scientists and provides the best resources for understanding data and analytics.”

The dependency tree of this sentence looks something like this-

In this tree, the root word is “community” having NN as the part of speech tag and every other word of this tree is connected to root, directly or indirectly, with a dependency relation such as a direct object, direct subject, modifiers, etc.

These relationships define their roles and functions of each word in the sentence and how multiple words are connected together. Every dependency can be represented in the form of a triplet which contains a governor, a relation, and a dependent,

which means that a dependent is connected to the governor by relation, or in other words, they are subject, verb, and object respectively. For example, in the same sentence: “Analytics Vidhya is the largest community of data scientists”

“Analytics Vidhya” is the subject and is playing the role of a governor, the verb here is “is” and is playing the role of the relation, and “the largest community of data scientist” is the dependent or the object.

Dependency grammars can be used in different use case-

Named Entity Recognition– they are used to solve named entity recognition problems.

Question Answering System– they can be used to understand relational and structural aspects of question-answering systems.

Coreference Resolution– they are also used in coreference resolutions in which the task is to map the pronouns to the respective noun phrases.

Text summarization and Text classification– they can also be used for text summarization problems and they are also used as features for text classification problems.

End Notes

In this article, we looked into the basics of Natural Language Processing.

NLP’s role in the modern world is skyrocketing. With the volume of unstructured data being produced, it is only efficient to master this skill or at least understand it to a level so that you as a data scientist can make some sense of it.

If you are interested in a full-fledged Natural Language Processing course covering everything from basics to extreme, here Analytics Vidhya’s Certified Natural Language Processing Master Program


Narrative Science: The Leader In Natural Language Generation Technology

Narrative Science helps people understand and communicate what is most important in their data. By transforming data into insightful, human-like language, the company’s natural language generation (NLG) technology enables people to be data-driven without needing to be data literate. As the leader in NLG for the enterprise, Narrative Science works with customers including Credit Suisse, Deloitte, MasterCard, USAA, and members of the U.S. intelligence community, empowering them to make better decisions, focus talent on higher-value opportunities, and create differentiated products– all through the power of data storytelling.

Narrative Science’s mission is to empower people to work smarter and realize their greatest potential by automatically transforming data into a powerful asset– information that is packed with relevant insights, written in conversational language, and generated at machine scale.

Transforming Data into Insightful Narratives

Unlike technology that requires the user to interpret and explain insights from data, Narrative Science’s NLG platform, Quill communicates the way humans do— analyzing the data to identify what’s interesting and important and then automatically transforming those insights into relevant, intuitive, and timely information— generating insightful narratives at a scale not possible by humans.

The company aims to make its technology as seamless as possible– easily integrating into the client’s existing data analytics and reporting environments, as well as enabling them to create their own digital products by incorporating NLG into their language interfaces.

Narrative Science is also dedicated to serving enterprises, with software that handles massive data volumes, adheres to complex reporting requirements, and has achieved Amazon Web Services (AWS) Competency Partner status and SOC 2 compliance– trusted by the world’s largest organizations in some of the most highly regulated industries.

An Inspiring Leader

Keelin McDonell serves as the Vice President of Professional Services at Narrative Science. She is dedicated to ensuring clients have exceptional experiences with the company and its technology. At Narrative Science, the team has led the charge of creating the new technology category of NLG. One of the things the company learned early-on regarding category creation is that the level of services and support needs to be higher because one really needs to prove the value. Keelin and her team make sure the software and value capture around the software is as high as possible by pioneering a services model that identifies the best ways to use NLG, evangelizing the value with the customer, and making sure Narrative Science is using the customers’ feedback to drive the road map for its products and services.

Keelin’s journey at Narrative Science began in 2011, as one of the company’s first employees, within the client services team. “As we were transitioning from a media-centric customer base to an enterprise-oriented one, we needed a larger services team with experience in domains such as financial services and the government to provide a more comprehensive experience for our clients. Since then, I’ve helped grow our services team from a handful of employees to a robust Customer Success department”, said Keelin. Her team not only creates NLG solutions for clients, but really acts as stewards to guide each client through the lifecycle of education, solution development, on-boarding, installation, support, and then continually ensuring they are receiving as much value as possible from our technology.

Delivering Exceptional Business Value to Enterprises

Narrative Science’s NLG technology delivers information from data that is relevant, intuitive, and timely– empowering people to be data-driven without needing to have expertise in data analysis or interpretation. By automating the communication of data insights into insightful narratives, the company help enterprises make better decisions, focus talent on higher-value opportunities, create differentiated products, and realize untapped potential.

As the leader in NLG, the company has developed different products and partnerships to help its enterprise customers. Narrative Science partners with its clients and co-develops language-as-an-interface products so they can engage with their customers. Narrative Science partners with leading BI and analytics companies, like Tableau, Microsoft, and Qlik, to integrate NLG into their visualization product interfaces, delivering insightful narratives alongside existing charts and graphs. Narrative Science partners with Global Alliance Partners, including Deloitte as part of their Deloitte Catalyst program, where it ideates NLG-powered use cases the company can use internally as well as bring to clients. Narrative Science also partners with Amazon Web Services (AWS) as part of its Machine Learning Competency program, where the company delivers cloud-based NLG solutions to some of the largest global enterprises.

Business Excellence and Global Recognition

In 2023, Fortune listed Narrative Science as one of the 50 companies leading the artificial intelligence revolution and CB Insights named Narrative Science to their top 100 most promising AI companies in the world. Additionally, Narrative Science received the Chicago Innovation Award which celebrates the most innovative products and services in Chicago. Narrative Science also received Amazon Web Services (AWS) Machine Learning Competency status owing to recognition for delivering innovative AI solutions to enterprise clients.

Challenges Executed by Narrative Science

Commenting on the challenges Keelin said, “Our biggest challenge, and opportunity, has been associated with creating the category of NLG. When we started, the technology was not well understood, so we needed to spend time upfront to first educate and then position the value. As the market has grown and awareness has spread, we can now focus more on the value upfront”.

The Future of Communication Powered by NLG

NLG is a disruptive technology that delivers the last mile in the big data and analytics spectrum by enabling machines to speak our language so that we don’t need to learn how to speak theirs. Narrative Science envisions a world where every data-driven communication will be powered by NLG. In the near-term, the adoption of NLG within business intelligence platforms and customer-facing applications will accelerate. The company is already making this happen through its integrations with data visualization platforms such as Qlik, Tableau, Microsoft Power BI, Sisense, MicroStrategy, and many others.

Fix Windows Keyboard Language Changes On Its Own In Windows 11/10

In the case of multiple keyboard scenarios, things get tricky. Keyboard language changes on its own because the keyboard changes while you are typing. The fact is that the keyboard changes because we accidentally hit shortcuts like WinKey+Space key (Changes language/keyboard pairs), or Alt+Shift Change language) or lastly Ctrl+Shift (Changes keyboard).  So, if your Windows 11/10 keyboard language changes on its own, there are three ways to resolve it. The first is to make sure Windows doesn’t add keyboards without your permission, and the second is what we are going to talk about next.

Fix Windows 11 keyboard language changes on its own

The primary solution to resolving the issue Windows 11 keyboard language changes on its own is the same for Windows 11 as for Windows 10, but the procedure is vastly different.

For the solution, you would have to set a different input method for each app window. The procedure to do so in Windows 11 is as follows:

In the Settings window, go to the Time and languages tab on the list on the left-hand side.

Now, under Switching input methods, check the box associated with Let me set a different input method for each app window.

Reboot your system.

How will this option help?

After checking the mentioned option, you would be prompted for the input method individually for each app, unlike earlier when the app could decide. This way, you wouldn’t be bothered with the automatic changing of the input language.

Why were the Language settings changed in Windows 11?

You must have noticed that Advanced keyboard settings fall under a separate setting menu named Typing. Obviously, it seems more appropriate and this is exactly why Windows 11 was created. The purpose was to change the Settings to a more accessible and appropriate structure. Rather, you won’t find most options at the same place with Windows 11.

Windows 10 keyboard language changes on its own Set a different input method for each app window

To disable the change in layout follows the steps below:

Type “Switching input methods”. This will open Advanced keyboard settings with this option inside.

Select the checkbox which says “Let me set a different input method for each app window”.

This will make sure that you choose which keyboard is going to be used for which app. Usually, when you change the keyboard when using an app, Windows remembers that so you don’t have to set it up again.

Apart from this, the Advanced keyboard settings page lets you override the default input method which could be different from the first one in your language list.  Lastly, there is a Langage bar option as well which comes in handy to switch between the keyboard rather quickly. In case you have removed it, you can always restore the language bar back.

Change Language Settings under the Administrative Tab

The last one was reported by a forum user with a solution. He reported that the problem occurred for him when he started typing in some programs with non-Unicode characters. Windows will use the default system local language to interpret these characters even if the language is not available in the language bar.

Make sure that all Display language, Input language, and Format are English (United States) or whatever you want to have. If not go back and make those changes.

Mark two checkbox in Copy your current settings to Welcome screen and system accounts & New user accounts

Last but not least, you may want to disable those shortcut combinations which can accidentally change your keyboard settings.

Having multiple keyboards on your Windows PC is tricky. But then many need to use them on a regular basis, and this change of keyboard is very annoying.

I hope these solutions worked for you!

Read: How to change Keyboard Layout in Windows.

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