Natural Language Processing NLP: What Is It & How Does it Work?


Complete Guide to Natural Language Processing NLP with Practical Examples

nlp analysis

Natural Language Processing (NLP) deals with how computers understand and translate human language. With NLP, machines can make sense of written or spoken text and perform tasks like translation, keyword extraction, topic classification, and more. Understanding human language is considered a difficult task due to its complexity. For example, there are an infinite number of different nlp analysis ways to arrange words in a sentence. Also, words can have several meanings and contextual information is necessary to correctly interpret sentences. Just take a look at the following newspaper headline “The Pope’s baby steps on gays.” This sentence clearly has two very different interpretations, which is a pretty good example of the challenges in natural language processing.

  • Computational linguistics is the science of understanding and constructing human language models with computers and software tools.
  • The final key to the text analysis puzzle, keyword extraction, is a broader form of the techniques we have already covered.
  • They may also have experience with programming languages such as Python, and C++ and be familiar with various NLP libraries and frameworks such as NLTK, spaCy, and OpenNLP.
  • This automation helps reduce costs, saves agents from spending time on redundant queries, and improves customer satisfaction.

Most of the work on adversarial text examples involves modifications at the character- and/or word-level; see Table SM3 for specific references. Other transformations include adding sentences or text chunks (Jia and Liang, 2017) or generating paraphrases with desired syntactic structures (Iyyer et al., 2018). In image captioning, Chen et al. (2018a) modified pixels in the input image to generate targeted attacks on the caption text.

Everyday NLP examples

A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work. • Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions. Natural Language Processing (NLP) makes it possible for computers to understand the human language. Behind the scenes, NLP analyzes the grammatical structure of sentences and the individual meaning of words, then uses algorithms to extract meaning and deliver outputs.

  • The simpletransformers library has ClassificationModel which is especially designed for text classification problems.
  • Our syntactic systems predict part-of-speech tags for each word in a given sentence, as well as morphological features such as gender and number.
  • Kia Motors America regularly collects feedback from vehicle owner questionnaires to uncover quality issues and improve products.

In August 2019, Facebook AI English-to-German machine translation model received first place in the contest held by the Conference of Machine Learning (WMT). The translations obtained by this model were defined by the organizers as “superhuman” and considered highly superior to the ones performed by human experts. Imagine you’ve just released a new product and want to detect your customers’ initial reactions. By tracking sentiment analysis, you can spot these negative comments right away and respond immediately. Although natural language processing continues to evolve, there are already many ways in which it is being used today.

Four techniques used in NLP analysis

Natural language processing and powerful machine learning algorithms (often multiple used in collaboration) are improving, and bringing order to the chaos of human language, right down to concepts like sarcasm. We are also starting to see new trends in NLP, so we can expect NLP to revolutionize the way humans and technology collaborate in the near future and beyond. Sentiment analysis (seen in the above chart) is one of the most popular NLP tasks, where machine learning models are trained to classify text by polarity of opinion (positive, negative, neutral, and everywhere in between).

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You can use is_stop to identify the stop words and remove them through below code.. In the same text data about a product Alexa, I am going to remove the stop words. While dealing with large text files, the stop words and punctuations will be repeated at high levels, misguiding us to think they are important.

While it is difficult to synthesize a holistic picture from this diverse body of work, it appears that neural networks are able to learn a substantial amount of information on various linguistic phenomena. These models are especially successful at capturing frequent properties, while some rare properties are more difficult to learn. Linzen et al. (2016), for instance, found that long short-term memory (LSTM) language models are able to capture subject–verb agreement in many common cases, while direct supervision is required for solving harder cases. Again, text classification is the organizing of large amounts of unstructured text (meaning the raw text data you are receiving from your customers). Topic modeling, sentiment analysis, and keyword extraction (which we’ll go through next) are subsets of text classification.

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The rise of deep learning has transformed the field of natural language processing (NLP) in recent years. Computational linguistics is the science of understanding and constructing human language models with computers and software tools. Researchers use computational linguistics methods, such as syntactic and semantic analysis, to create frameworks that help machines understand conversational human language. Tools like language translators, text-to-speech synthesizers, and speech recognition software are based on computational linguistics. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.


Recent advances in deep learning, particularly in the area of neural networks, have led to significant improvements in the performance of NLP systems. Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to tasks such as sentiment analysis and machine translation, achieving state-of-the-art results. Bi-directional Encoder Representations from Transformers (BERT) is a pre-trained model with unlabeled text available on BookCorpus and English Wikipedia. This can be fine-tuned to capture context for various NLP tasks such as question answering, sentiment analysis, text classification, sentence embedding, interpreting ambiguity in the text etc. [25, 33, 90, 148].

nlp analysis

Overall, NLP is a rapidly evolving field that has the potential to revolutionize the way we interact with computers and the world around us.

Getting started with NLP and Talend

Thus, the cross-lingual framework allows for the interpretation of events, participants, locations, and time, as well as the relations between them. Output of these individual pipelines is intended to be used as input for a system that obtains event centric knowledge graphs. All modules take standard input, to do some annotation, and produce standard output which in turn becomes the input for the next module pipelines. Their pipelines are built as a data centric architecture so that modules can be adapted and replaced. Furthermore, modular architecture allows for different configurations and for dynamic distribution. A subfield of NLP called natural language understanding (NLU) has begun to rise in popularity because of its potential in cognitive and AI applications.

There have also been huge advancements in machine translation through the rise of recurrent neural networks, about which I also wrote a blog post. Gathering market intelligence becomes much easier with natural language processing, which can analyze online reviews, social media posts and web forums. Compiling this data can help marketing teams understand what consumers care about and how they perceive a business’ brand.