NLP Technical Description

Natural Language Processing is an Artificial Intelligence branch assisting computers in comprehending, manipulating, and interpreting human language (Schmitt et al, 2019). Natural language processing draws from several disciplines, which include computational linguistics, and computer science, in its pursuance of filling the disparity between computer comprehension and human communication. While Natural Language Processing is not a new science, the automation quickly advances, and this has ensured an expanded interest in human-machine interaction, big data accessibility, advanced algorithms, and authoritative computing. A human can write and speak in Chinese, Spanish, or English, but the native language of the computer, normally referred to as machine language or machine code, is incomprehensible broadly to most human beings. Conversation take place not with words but via millions of ones and zeros, producing logical action at user’s lowest level of the device. Computers can work with standardized and structured data such as financial records and database tables. They can process data much quicker than humans can. People do not communicate in "structured data" nor speak binary. They can communicate by use of words, a form of unstructured data. Humans can get machines closer to that deeper comprehension of language human level with the help of NLP algorithms. Nowadays, NLP allows people to build things, such as automated systems, language translators, and chatbot that can recommend the best Netflix television shows.

Natural Language Processing is a step in a broad mission for the automation filed like usage of AI to help facilitate the manner with which world works. The automated world has showed to be a game-changer for several organizations as a raising technology-savvy. People adopts new methods by which they can interact online with others and also organizations. Social platforms has reformulated the community definition; cryptocurrency has advances the digital payment norm; cloud storage has introduced another data retention level to the masses; and electronic commerce has developed a new meaning of convenience. Through artificial intelligence, fields such as machine learning are opening eyes to a world of all potentialities. Increasingly, machine learning is utilize in data analytics to assist in making sense of big data. Also, it is used in program chatbot, thus stimulating human communication with customers. Nonetheless, these forward machine-learning applications would only be possible with NLP improvisation (Chowdhary, & Chowdhary, 2020).

Natural language processing combines artificial intelligence with computational linguistics and computer science to process natural or human speech and languages. The process can be grouped into three tasks. The 1^st NLP function is to comprehend the natural language accepted by the computer. The machine utilizes a built-in statistical exemplary to execute speech recognition, thus converting the natural language to a language which is programmable. It works on this as it breaks down a current speech into small units and then contrast them with previous ones from a previous speech. The result or output in text format actualize the sentences and words statistically that were most said. Thus 1^st function is known as the speech-to-text process (Chiche, & Yitagesu, 2022).   POS (part-of-speech) is the next task (Chiche, & Yitagesu, 2022). It is a word-category or tagging formulation. This process identifies words elementarily in their linguistic forms as adjectives, past tense, verbs, and nouns, to mention a few, that use lexicon rule set coded into the machine. After these two processes, the computer probably now comprehends the speech meaning that was made. Text-to-speech conversion is the 3^rd step taken by natural language processing. At this stage, the computer programming language is transformed into a textual or audible format for the user. A financial news chatbot, for instance, that asks a question such as "How is Google doing today?" will mostly scan online finance sites for Google stock and can make a decision to choose only information such as volume and price as its response.

Resources Required for NLP Implementation

There are several resources needed for NLP implementation. Some of them are discussed. The first resource is a dataset or corpus. Corpus in NLP contains speech and text data that can be utilized to train machine learning systems and artificial intelligence ( NLP - linguistic resources. Online Courses and eBooks Library, n.d.). If a user has a certain objective or challenge they need to address, they will require a collection of supporting data or at least a representation of what they want to achieve with NLP and machine learning. However, most machines must be equipped to comprehend language and its surrounding intention or content. As a result, natural language annotation is crucial for developing structured training data enabling machines to comprehend human speech for tasks like summarization or question answering.

Pre-trained model is the second resource (Qiu et al, 2020). Natural language processing pre-trained models are helpful for tasks of NLP, such as predicting missing parts of a sentence, translating text, or even generating new sentences. Pre-trained models can be utilized in natural languages processing applications like NLP API and chatbot. Users can utilize several kinds of pre-trained models to get started with NLG (Natural Language Generation) or Neural Machine Translation (NMT), text summarization, or NER, depending on the project's needs.

NLP Frameworks and Libraries are the last. Many popular open-source NLP frameworks and libraries are available, like spaCy and NLTK. Natural language toolkit is one of the leading frameworks that assist in developing Python programs to analyze and manage human language data (NLTK) (Schmitt et al, 2019). NLTK documentation offers wrappers for powerful natural language processing libraries, intuitive access, and a lively community to several lexical and corpora resources, which include WordNet. Additionally, it offers a suite of text-processing libraries for semantic reasoning, categorization, parsing, tokenization, tagging, and stemming. NLP with Python assists people in comprehending the framework. It can provide a very helpful way of writing code, thus solving issues related to natural processing issues. Spacy is a library that can be utilized with both Python and Python. It is an NLTK development incorporating pre- training statistical models and word vectors. Tokenization is supported in several languages. This library is one of the best for working with tokenization. The text can be broken into semantic units such as punctuation, articles, and words. All of the functionality required for projects in the real world is present in SpaCy. The libraries provide tools, algorithms, and pre- built functions for tasks like sentiment analysis, tokenization, named entity recognition, and part-of-speech tagging, to mention a few.

Operational Benefits of NLP IN IBM

IBM Corporation has been able to put focus on expanding and developing enterprise NLP capabilities designed to assist businesses in answering questions, unearthing insights, and making more informed decisions even with a lack of expertise or a small data set. This corporation has upgraded to IBM Watson Discovery's NLP capabilities (Carvalho et al, 2019). IBM's upgrade has benefited corporate insurance, financial, and legal users. Via the insights discovery, these upgrades are leveraged, thus enhancing customer service and accelerating business operations. IBM is increasingly turning to machine learning and NLP to help the organization sift through increasing amounts of data sets and documents in several formats. Business people can reduce research time by leveraging artificial intelligence to extract insights from documents. In addition, it can enable workers to make more fact-based decisions, particularly as they perform time-sensitive, complex tasks like conducting financial analyses and processing insurance claims. Several tools released by the IBM corporations make it simple for Watson discovery users to swiftly tailor the underlying natural language processing models to their unique language of the business. As an outcome of IBM resource advances in NLP, business people can teach Watson discovery to help them in surfacing, comprehending, and reading; additionally, more precise insights from industry-specific documents and massive, complicated quantities.

Challenges and Issues arising due to NLP Implementation

Natural language is ambiguous, making developing computer applications that completely comprehend human language very challenging (Sintoris, & Vergidis, 2017, July). Finding and collecting enough high-quality data to test and train models is one of the primary challenges of NLP implementation that IBM Corporation can face. Data acts as the fuel of natural language processing, and with it, IBM models will deliver accurate outcomes and perform well. However, data is usually scarce, outdated, incomplete, noisy, or biased. Furthermore, data can be subject to security and privacy regulations, thus limiting usage and access. Therefore, businesses must ensure that they have a clear data technique helping them to source data from diverse and reliable sources, comply with relevant ethical and law standards, and preprocess and clean data properly. Dealing with the diversity and complexity of human language is another challenge NLP faces during implementation in IBM. Language is not a uniform or fixed system but rather an evolving and dynamic one. It has several varieties, like sarcasm, dialects, jargon, accents, idioms, and slang. Also, it has various ambiguities, like metaphors, homonyms, anaphora, and synonyms. In addition, language is influenced by the writers or speaker's emotions, intention, tone, and context. Therefore, IBM must make sure that models can handle the subtleties and nuances of language, adapt to distinct scenarios and domains, and capture the sentiment and meaning behind the words. Deploying and integrating models into existing workflows and systems is NLP's 3^rd and last challenge. Models of NLP are not standalone solutions but elements of larger systems interacting with other components like analytic tools, user interfaces, APIs, and databases. Therefore, IBM must ensure the models are interoperable and compatible with the systems. This will enable handling output and input channels and formats, perform and scale well under distinct conditions and loads, and IBM needs to maintain and update easily.

Future Potential of NLP in IBM

NLP's future is a little unpredictable, but it is broadly known that it will be a part of IBM's daily lives in the next few years. NLP is the comprehension of the natural human language process. In other words, computers and machines can comprehend human language. The NLP future is to have machines that can comprehend and have a general comprehension of human language (Agrawal, 2020, September 3). This would permit IBM Corporation to interact with machines in ways that they do with other humans. Language constantly evolves, and IBM is needed to meet the changing community dynamics. The business world is no different; its vernacular is evolving in response to innovations, world events, and changing consumer expectations. The business language is documented in several enterprise forms, which start from simple text to more challenging formats like images, PDFs, tables, and charts, and every company modifies the business language to suit its requirements. With the correct tools for interpretation, the meaning of the language will be preserved. Natural language processing in the future will be fundamental since it will interpret the insights and trends hidden within enterprise data. For IBM's future, it plans to design new natural language processing to assist business users in companies like legal services, insurance, and financial services, thus enhancing customer care and accelerating business processes by synthesizing information and uncovering insights from complicated documents (Agrawal, 2020, September 3).

Real-Life Examples of How Organizations Utilize NLP

Several organizations are utilizing NLP, and some of them are Microsoft Corporation and Apple Inc. Microsoft Corporation has been developing and reaching NLP (Natural Language Processing) technologies since the late 1990s. Natural language processing technology has enabled computers to comprehend and interpret natural language. This has enabled them to interact with humans more naturally (Livingston, 2022, September 22). Microsoft has been working on several NLP aspects, like sentiment analysis, machine translation, text analytics, speech recognition, and conversational agents. Also, the organization has created many applications utilizing natural languages processing technology like Office products such as Word, Outlook, Skype Translator, and Cortana Virtual Assistant. NLP technology of Microsoft has been used in several industries, which include hospitality, healthcare, finance, travel, and retail. The organization has also collaborated with various learning institutions to advance its research into NLP further.

Apple Inc is one of the biggest organizations worldwide and has been a leader in developing several technologies, including NLP. This company has been using NLP to power its Siri voice assistant, which can respond to and comprehend natural language queries. In addition, Apple Company has been able to implement NLP in their photos app, thus detecting objects, science, and animal categories. Also, it utilizes NLP to power its QuickType keyboard, which predicts phrases and words as the user types. Finally, the company has used NLP for its App Store search engine to assist users in finding apps easily and quickly. The use of NLP by Apple is just an example of how the technology can help improve user experience, thus making their lives easier.

References

• Agrawal, D. (2020, September 3). The next generation of NLP: How new capabilities empower businesses to make data-informed decisions. IBM Blog. https://www.ibm.com/blog/the- next-generation-of-nlp-how-new-capabilities-empower-businesses-to-make-data-informed- decisions/
• Carvalho, A., Levitt, A., Levitt, S., Khaddam, E., & Benamati, J. (2019). Off-the-shelf artificial intelligence technologies for sentiment and emotion analysis: a tutorial on using IBM natural language processing. Communications of the Association for Information Systems, 44(1), 43.
• Chiche, A., & Yitagesu, B. (2022). Part of speech tagging: a systematic review of deep learning and machine learning approaches. Journal of Big Data, 9(1), 1-25.
• Dunn, J. (2022). Natural language processing for corpus linguistics. Cambridge University Press. Livingston, Z. (2022, September 22). Top natural language processing companies 2022. eWEEK.
• https://www.eweek.com/big-data-and-analytics/natural-language-processing-companies/ NLP - linguistic resources. Online Courses and eBooks Library. (n.d.). https://www.tutorialspoint.com/natural_language_processing/natural_language_processin g_linguistic_resources.htm.