What is NLP? How it Works, Benefits, Challenges, Examples

This is what we call homonyms, two or more words that have the same pronunciation but have different meanings. This can make tasks such as speech recognition difficult, as it is not in the form of text data. These are easy for humans to understand because we read the context of the sentence and we understand all of the different definitions. And, while NLP language models may have learned all of the definitions, differentiating between them in context can present problems. Another approach is to develop contextual and domain-specific models that can capture the nuances and complexities of language in specific contexts and domains.

Chatbots can also integrate other AI technologies such as analytics to analyze and observe patterns in users’ speech, as well as non-conversational features such as images or maps to enhance user experience. And depending on the chatbot type (e.g. rule-based, AI-based, hybrid) they formulate answers in response to the understood queries. The field of nlp challenges Natural Language Processing (NLP) has evolved with, and as well as influenced, recent advances in Artificial Intelligence (AI) and computing technologies, opening up new applications and novel interactions with humans. Considering these metrics in mind, it helps to evaluate the performance of an NLP model for a particular task or a variety of tasks.

Inability to Handle Complex Sentences

The use of NLP has become more prevalent in recent years as technology has advanced. Personal Digital Assistant applications such as Google Home, Siri, Cortana, and Alexa have all been updated with NLP capabilities. NLP is useful for personal assistants such as Alexa, enabling the virtual assistant to understand spoken word commands. It also helps to quickly find relevant information from databases containing millions of documents in seconds. An NLP-generated document accurately summarizes any original text that humans can’t automatically generate. Also, it can carry out repetitive tasks such as analyzing large chunks of data to improve human efficiency.

If you feed the system bad or questionable data, it’s going to learn the wrong things, or learn in an inefficient way. However, these challenges are being tackled today with advancements in NLU, deep learning and community training data which create a window for algorithms to observe real-life text and speech and learn from it. In this paper, we provide a short overview of NLP, then we dive into the different challenges that are facing it, finally, we conclude by presenting recent trends and future research directions that are speculated by the research community. Luong et al. [70] used neural machine translation on the WMT14 dataset and performed translation of English text to French text.

Major Challenges of Using Natural Language Processing

Also, NLP has support from NLU, which aims at breaking down the words and sentences from a contextual point of view. Finally, there is NLG to help machines respond by generating their own version of human language for two-way communication. The most popular technique used in word embedding is word2vec — an NLP tool that uses a neural network model to learn word association from a large piece of text data. However, the major limitation to word2vec is understanding context, such as polysemous words. The language has four tones and each of these tones can change the meaning of a word.

If you’ve laboriously crafted a sentiment corpus in English, it’s tempting to simply translate everything into English, rather than redo that task in each other language. One of the challenges with NLP is not just measuring accuracy via an F1 score, but also looking at things like biases, inclusiveness, and “black holes” that the models miss. Therefore, several talks at the event focus on testing and understanding how NLP models perform on Responsible AI questions.

Creating and maintaining natural language features is a lot of work, and having to do that over and over again, with new sets of native speakers to help, is an intimidating task. It’s tempting to just focus on a few particularly important languages and let them speak for the world. A company can have specific issues and opportunities in individual countries, and people speaking less-common languages are less likely to have their voices heard through any channels, not just digital ones. One way the industry has addressed challenges in multilingual modeling is by translating from the target language into English and then performing the various NLP tasks.

Humans produce so much text data that we do not even realize the value it holds for businesses and society today. We don’t realize its importance because it’s part of our day-to-day lives and easy to understand, but if you input this same text data into a computer, it’s a big challenge to understand what’s being said or happening. Analytics Insight® is an influential platform dedicated to insights, trends, and opinion from the world of data-driven technologies.

There are particular words in the document that refer to specific entities or real-world objects like location, people, organizations etc. To find the words which have a unique context and are more informative, noun phrases are considered in the text documents. Named entity recognition (NER) is a technique to recognize and separate the named entities and group them under predefined classes. But in the era of the Internet, where people use slang not the traditional or standard English which cannot be processed by standard natural language processing tools. Ritter (2011) [111] proposed the classification of named entities in tweets because standard NLP tools did not perform well on tweets. Several companies in BI spaces are trying to get with the trend and trying hard to ensure that data becomes more friendly and easily accessible.

• Since simple tokens may not represent the actual meaning of the text, it is advisable to use phrases such as “North Africa” as a single word instead of ‘North’ and ‘Africa’ separate words.
• Moreover, this type of neural network architecture ensures that the weighted average calculation for each word is unique.
• Some of these tasks have direct real-world applications such as Machine translation, Named entity recognition, Optical character recognition etc.
• The potential for NLP to transform industries and improve human-machine communication is enormous, and we can expect to see significant progress in the coming years.
• Neural networks can be used to anticipate a state that has not yet been seen, such as future states for which predictors exist whereas HMM predicts hidden states.

Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day. NLP models must be able to handle multiple languages and dialects, each with its own unique structure, grammar, and vocabulary. However, language variations can also pose a challenge, as words can have different meanings and usage depending on the region, culture, or context. The software would analyze social media posts about a business or product to determine whether people think positively or negatively about it.

Real-Time Processing and Efficiency

It’s task was to implement a robust and multilingual system able to analyze/comprehend medical sentences, and to preserve a knowledge of free text into a language independent knowledge representation [107, 108]. NLP hinges on the concepts of sentimental and linguistic analysis of the language, followed by data procurement, cleansing, labeling, and training. Yet, some languages do not have a lot of usable data or historical context for the NLP solutions to work around with. Therefore, despite NLP being considered one of the more reliable options to train machines in the language-specific domain, words with similar spellings, sounds, and pronunciations can throw the context off rather significantly.