"Lumenore" Virtual Internship Program

“Lumenore” Virtual Internship Program. Lumenore Certified NLQ Analyst.

[Audio] Hello Everyone Welcome to the Lumenore Certified NLQ Analyst course..

[Audio] The most widely utilized NLQ application is machine translation which assists with conquering language obstructions. As the amount of data accessible online is expanding step by step, the need to access and process it turns out to be increasingly significant. To convert data from one language then onto the next, machine translation can be utilized. The NLQ methods help the machine comprehend the significance of sentences, which improves the effectiveness of machine translation..

[Audio] The NLP methods are extremely valuable for sentiment analysis. It assists in recognizing the sentiment among several online posts and comments. Business firms utilize NLP methods to learn about the customer's opinions about their product and services from online reviews..

Large Volumes O f T extual D ata. 5. Measure Sentiment Language-based data Unstructured data Fully analyze text Speech data efficiently.

[Audio] Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms, and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages. While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there's also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLQ is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics..

[Audio] Businesses use massive quantities of unstructured, text-heavy data and need a way to efficiently process it. A lot of the information created online and stored in databases is natural human language, and until recently, businesses could not effectively analyze this data. This is where natural language processing is useful..

[Audio] The advantage of natural language query can be seen when considering the following two statements: " Cloud computing insurance should be part of every service-level agreement," and, "A good SLA ensures an easier night's sleep -- even in the cloud." If a user relies on natural language query for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing, and that SLA is an industry acronym for service-level agreement. These are the types of vague elements that frequently appear in human language and that machine learning algorithms have historically been bad at interpreting. Now, with improvements in deep learning and machine learning methods, algorithms can effectively interpret them. These improvements expand the breadth and depth of data that can be analyzed..

[Audio] Syntax and semantic analysis are two main techniques used with natural language Query. The syntax is the arrangement of words in a sentence to make grammatical sense. NLQ uses syntax to assess meaning from a language based on grammatical rules. Whereas semantics involves the use of and meaning behind words. Natural language processing applies algorithms to understand the meaning and structure of sentences.

[Audio] The biggest benefit of NLQ for businesses is the ability of technology to detect, and process massive volumes of text data across the digital world including; social media platforms, online reviews, news reports, and others. Also, by collecting and analyzing business data, NLP is able to offer businesses valuable insights into brand performance. In addition, NLP models can detect any persisting issues and take necessary mitigation measures to improve performance. Google speech to text is able to achieve all of this by training machines to understand human language in a faster, more accurate, and consistent way than human agents. The technology is able to consistently monitor and process data. This helps brands remain updated with their online presence, and not get riddled with inconsistencies..

[Audio] Parsing. This is the grammatical analysis of a sentence. Example: A natural language query algorithm is fed the sentence, "The dog barked." Parsing involves breaking this sentence into parts of speech -- i.e., dog = noun, barked = verb. This is useful for more complex downstream processing tasks. Word segmentation. This is the act of taking a string of text and deriving word forms from it. Example: A person scans a handwritten document into a computer. The algorithm would be able to analyze the page and recognize that the words are divided by white spaces. Sentence breaking. This places sentence boundaries in large texts. Example: A natural language query algorithm is fed the text, "The dog barked. I woke up." The algorithm can recognize the period that splits up the sentences using sentence breaking. Morphological segmentation. This divides words into smaller parts called morphemes. Example: The word untestable would be broken into [[un[[ test]able]]ly], where the algorithm recognizes " un," "test," "able" and " ly" as morphemes. This is especially useful in machine translation and speech recognition. Stemming. This divides words with inflection in them to root forms. Example: In the sentence, "The dog barked," the algorithm would be able to recognize the root of the word "barked" is " bark." This would be useful if a user was analyzing a text for all instances of the word bark, as well as all of its conjugations. The algorithm can see that they are essentially the same word even though the letters are different..

[Audio] Semantic Word sense disambiguation. This derives the meaning of a word based on context. Example: Consider the sentence, "The pig is in the pen." The word pen has different meanings. An algorithm using this method can understand that the use of the word pen here refers to a fenced-in area, not a writing implement. Named entity recognition This determines words that can be categorized into groups. Example: An algorithm using this method could analyze a news article and identify all mentions of a certain company or product. Using the semantics of the text, it would be able to differentiate between entities that are visually the same. For instance, in the sentence, " Daniel McDonald's son went to McDonald's and ordered a Happy Meal," the algorithm could recognize the two instances of "McDonald's" as two separate entities – one a restaurant and one a person. Natural language generation. This uses a database to determine the semantics behind words and generate new text. Example: An algorithm could automatically write a summary of findings from a business intelligence platform, mapping certain words and phrases to features of the data in the BI platform. Another example would be automatically generating news articles or tweets based on a certain body of text used for training..

[Audio] Thank you everyone. THANK YOU.