What is Natural Language Processing? Definition and Examples

natural language processing problems

This makes it very rigid and less robust to changes in the nuances of the language and also required a lot of manual intervention. Deep learning techniques allow for a more flexible approach and lets the model learn from examples. While natural language processing isn’t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.

natural language processing problems

We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. https://www.metadialog.com/ It is a known issue that while there are tons of data for popular languages, such as English or Chinese, there are thousands of languages that are spoken but few people and consequently receive far less attention.

Natural Language Processing (NLP) Challenges

Due to the gradient disappearance defect of recurrent neural network (RNN), Long Short-Term Memory (LSTM) was proposed [10]. Due to the recent popularity of deep learning methods, LSTM has also been applied to work such as dialogue systems [11] and language models [12]. The neural network model with attention mechanism proposed recently [13] has attracted the attention of researchers. This attention mechanism has been successfully applied to machine translation [14] and text summaries [15] and has achieved certain results. In the 1940s, researchers introduced the term “neural network” in order to express biological information processing systems [6].

natural language processing problems

The good news is that NLP has made a huge leap from the periphery of machine learning to the forefront of the technology, meaning more attention to language and speech processing, faster pace of advancing and more innovation. The marriage of NLP techniques with Deep Learning has started to yield results — and can become the solution for the open problems. Users learn new words, phrases, and expressions on a daily basis, while others forget. It is challenging for Natural Language Processing systems to stay current and handle new language advances appropriately. It is challenging to keep up with the development since NLP algorithms must constantly update their language models.

Major Challenges of NLP Every Business Leader Should Know

Given the rapid advances in the field and the interdisciplinary nature of NLP, this is a daunting task. Furthermore, new datasets, software libraries, applications frameworks, and workflow systems will continue to emerge. Nonetheless, we expect that this chapter will serve as starting point for readers’ further exploration by using the conceptual roadmap provided in this chapter. For the unversed, NLP is a subfield of Artificial Intelligence capable of natural language processing problems breaking down human language and feeding the tenets of the same to the intelligent models. NLP, paired with NLU (Natural Language Understanding) and NLG (Natural Language Generation), aims at developing highly intelligent and proactive search engines, grammar checkers, translates, voice assistants, and more. Yet, in some cases, words (precisely deciphered) can determine the entire course of action relevant to highly intelligent machines and models.

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Your device activated when it heard you speak, understood the unspoken intent in the comment, executed an action and provided feedback in a well-formed English sentence, all in the space of about five seconds. The complete interaction was made possible by NLP, along with other AI elements such as machine learning and deep learning. Xie et al. [154] proposed a neural architecture where candidate answers and their representation learning are constituent centric, guided by a parse tree. Under this architecture, the search space of candidate answers is reduced while preserving the hierarchical, syntactic, and compositional structure among constituents.


Another common use of NLP is for text prediction and autocorrect, which you’ve likely encountered many times before while messaging a friend or drafting a document. This technology allows texters and writers alike to speed-up their writing process and correct common typos. Some of the most common ways NLP is used are through voice-activated digital assistants on smartphones, email-scanning programs used to identify spam, and translation apps that decipher foreign languages. It can be seen from the data in the table that the dual attention mechanism can effectively reduce the number of errors in the output results. In the effective output, the F1 value of the model reached 0.827, and its change with the training process is shown in Figure 5.

Autocorrect and grammar correction applications can handle common mistakes, but don’t always understand the writer’s intention. Even for humans this sentence alone is difficult to interpret without the context of surrounding text. POS (part of speech) tagging is one NLP solution that can help solve the problem, somewhat. The same words and phrases can have different meanings according the context of a sentence and many words – especially in English – have the exact same pronunciation but totally different meanings. Natural Language Processing is an area of computer science focused on leveraging natural language in human-computer communication.

In addition, the model models the analysis process of the entire sentence in the dependent syntax analysis and improves the greedy model to model the independent analysis state. The experimental results show that compared with the baseline method, the model obtains an improvement of 0.6 to 0.7 percentage points. Earlier machine learning techniques such as Naïve Bayes, HMM etc. were majorly used for NLP but by the end of 2010, neural networks transformed and enhanced NLP tasks by learning multilevel features. Major use of neural networks in NLP is observed for word embedding where words are represented in the form of vectors. Initially focus was on feedforward [49] and CNN (convolutional neural network) architecture [69] but later researchers adopted recurrent neural networks to capture the context of a word with respect to surrounding words of a sentence. LSTM (Long Short-Term Memory), a variant of RNN, is used in various tasks such as word prediction, and sentence topic prediction.

Because as formal language, colloquialisms may have no “dictionary definition” at all, and these expressions may even have different meanings in different geographic areas. Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day. Synonyms can lead to issues similar to contextual understanding because we use many different words to express the same idea. Furthermore, some of these words may convey exactly the same meaning, while some may be levels of complexity (small, little, tiny, minute) and different people use synonyms to denote slightly different meanings within their personal vocabulary.

Statistical approach

With the advent of big data, data-driven approaches to NLP problems ushered in a new paradigm, where the complexity of the problem domain is effectively managed by using large datasets to build simple but high quality models. Naive Bayes is a probabilistic algorithm which is based on probability theory and Bayes’ Theorem to predict the tag of a text such as news or customer review. It helps to calculate the probability of each tag for the given text and return the tag with the highest probability.


This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. The classifier can not only classify the current pattern feature but also multirich information natural language processing problems such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.