What Is the Purpose of NLP in Artificial Intelligence?

Artificial Intelligence (AI) has a subfield called Natural Language Processing (NLP), which aims to equip computers with the ability to interpret and manipulate human language. NLP seeks to create applications that can correct spellings, translate languages, and classify content by subject. In today’s data-driven world, NLP is a critical technology that businesses utilise to extract valuable insights and streamline tedious operations.

The topic of this blog post is exploring how artificial intelligence leverages natural language processing.

Artificial intelligence capabilities that enable natural language processing

NLP comprises of two main components, which are:

Automatic Language Generation (NLG)

Natural Language Generation (NLG) is a technology that automatically produces written language from data. The process involves three phases: text outlining, sentence structuring, and content realisation. During the outlining phase, a plan is created to guide the creation of text. In the second phase, sentences are formulated while considering grammar, meaning, and context. Finally, the content produced is revised to ensure accuracy and coherence, and it reflects the desired meaning.

  • Content strategy:

    Gathering appropriate data.
  • Sentence Planning:

    Crafting articulate language and establishing emotional context in a statement.
  • Text Realisation:

    Assembling a statement from a plan.

The applications of Natural Language Generation (NLG) are wide-ranging, including chatbots, machine translation systems, analytics platforms, voice assistants, sentiment analysis programs, and Artificial Intelligence (AI) based transcription software. NLG is an incredibly powerful technology that enhances the capabilities of both individuals and businesses alike.

Applying the Inherent Patterns of Language (NLU)

Natural Language Understanding (NLU) empowers computers to comprehend and interpret human speech by extracting meaningful information from the provided material. NLU enables computers to understand the meaning of spoken and written words, place them in proper context, and draw accurate conclusions. With NLU, computers are capable of responding to inquiries, recognising spoken commands, and responding with natural language responses. NLU also allows computers to reason, make decisions, and provide explanations based on the available data.

  • Beneficial for analysing language in all of its aspects.
  • Assists in mapping input in natural language into meaningful representations.

Tasks involving NLU are more challenging than those involving NLG because of referential, lexical and syntactic ambiguity.

  • Ambiguous Language:

    Consequently, a single word can have multiple interpretations. For instance, the word “male” might represent someone seeking a life partner, while the term “match” could have a dual meaning, signifying both a potential partner or a competitor in this context, resulting in a certain level of ambiguity.
  • Syntactic Ambiguity:

    This term refers to a phrase with multiple interpretations. For instance, in the instance of a fish, it could be understood as being prepared for consumption or as being ready to be eaten by someone else, which can be confusing. To alleviate this ambiguity, a part-of-speech tagging approach is employed to distinguish between the two meanings and provide clarity.
  • Referential Ambiguity:

    When Tom met both Jerry and John recently, the three of them decided to watch a film together. Nevertheless, the pronoun “they” utilised in the text is ambiguous and may refer to Tom, Jerry and John, or some other unidentified group. This ambiguity could result in confusion regarding who is being referred to.

The Natural Language Processing Pipeline is the procedure for processing natural language in artificial intelligence.

The Natural Language Processing Pipeline comprises a range of procedures to decipher human speech.

Sentence Segmentation is the Initial Step

Sentence segmentation serves as the opening stage in natural language processing. By dividing a paragraph into individual sentences, the meaning of the overall piece is clarified. For instance, London is the capital and largest city in England and the United Kingdom. Situated in the southeast of Great Britain, on the banks of the River Thames, London has been a crucial population centre for almost two millennia. The Romans established the settlement and called it Londinium.

Information Sourced from Wikipedia

The following is the result obtained through the use of sentence segmentation:

  1. It is accurate to state that “London is the most populous city in England and the United Kingdom.”
  2. “London has been a significant city for two millennia. It is situated on the southeastern corner of the island of Great Britain, on the banks of the River Thames.”
  3. The city was founded by the Romans and was named Londinium.

Part 2: Word Tokenization

Word tokenization involves breaking down a phrase into its constituent words, which can be analysed more efficiently to enhance comprehension of the text’s meaning. For instance, the sentence “London is the capital and most populous city of England and the United Kingdom” can be tokenized into its individual parts. This enables a more profound understanding of the text’s context.

Stemming: The Third Phase

Stemming is a valuable technique in the text processing pre-processing phase. It involves breaking down a phrase into individual parts to decipher its meaning.

Stemming is the act of simplifying words to their basic forms, or stems. This can be helpful in making informed assumptions about the grammatical functions of the tokens. For instance, the words “intelligently,” “intelligence,” and “intelligent” can all be reduced to their root word, “intelligent.” It is worth noting, though, that “intelligent” is not an appropriate term in English, despite its widespread use.


Lemmatization involves reducing a word to its simplest form by eliminating inflectional suffixes like “-ed”, “-ing”, “-s”, etc. This is comparable to stemming, but lemmatization produces a legitimate word instead of a stem that might not be an authentic word. “Play” is a good example since it has various spellings and interpretations; thus, the lemma of these phrases would be “play.” Unlike if we analyzed the word “intelligent,” where the root doesn’t represent a legitimate word.

Stop the Word Analysis

The subsequent stage of the natural language processing (NLP) pipeline is to assess the significance of each word in a sentence. Words that are commonly used, such as “is,” “a,” “the,” and “and,” are classified as stop words because they appear frequently. Thus, these stop words are excluded from consideration to draw attention to essential expressions.

Parsing Dependencies: The Sixth Phase

The subsequent phase of our process is Dependency Parsing, a technique utilized to recognize and assess the connections between the words in a sentence. A tree can be constructed using this method to identify the dependent nodes, with a single word serving as the root node. Usually, the root node is the verb that governs the phrase.

Language Annotation Utilizing Part-of-Speech Tags

POS tags are designed to incorporate words such as verbs, adverbs, nouns, and adjectives so that they can be communicated correctly within a sentence.

When a computer is given the task of comprehending human speech, it employs Natural Language Processing (NLP), a technique that is useful in extracting meaning from text data. This permits computers to automate various tasks, such as spell-checking, translations, and the monitoring of social media conduct. In today’s digital era, NLP is a vital tool that enables computers to interact with humans in a variety of ways.

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