Text is all around us—in books, papers, blogs, tweets, the news, and so on. Being so flooded with text, many questions surface about how to develop machine learning models for text data.
Working with text is challenging since it calls for understanding from several domains, including machine learning, linguistics, statistical natural language processing, and deep learning.
Deep learning techniques have revolutionized Natural Language Processing, offering more effective methods for text prediction and analysis. To understand the core principles behind these advancements, explore this detailed explanation of Natural Language Processing.
There are also specific problems that come along with text modeling—it is messy, and machine learning algorithms revolve more around defined fixed-length inputs and outputs. Specifically, machine learning algorithms are not directly used with raw texts because it requires them to be first converted into vectors and numbers.
This is called feature encoding or also known as feature extraction, and particularly in this area, deep learning is genuinely changing things. Traditional linguistic techniques for natural language processing needed linguists to define rules to cover particular cases. These were frail but effective in small spaces.
Nowadays, the use of NLP with deep learning has increased because of practical results and more straightforward methods. Many tools are now developed that can process your text in many different ways.
We have recently had our hands on an AI-powered paraphrasing tool; a free tool that can paraphrase (make your text unique), eliminate grammatical errors, and condense long documents. Students, businesses, and marketers widely use such tools.
Natural language processing and Deep Learning
Statistical methods outperform traditional language methods by learning rules and models from data rather than needing them to be specified top-down. They produce significantly better performance, but to get relevant and useful results, they still need to be hand-crafted augmentations by linguists.
A pipeline of statistical techniques is often necessary to get a single modeling outcome, as in the case of machine translation. With single and simpler models, deep learning techniques are beginning to out-compete statistical techniques on some problematic natural language processing issues.
Deep learning is a set of algorithms used to compute large amounts of data using artificial neural networks sophistically. It is a kind of machine learning used to achieve results based on the function and structure of the human brain.
Deep learning is quite common nowadays because of simple algorithms and magical results. Industries like health care, entertainment, advertising, and many others commonly use deep learning for their benefit.
Deep learning has become extremely popular in scientific computing, and businesses that deal with complicated issues frequently employ its techniques. All algorithms of deep learning perform tasks using variant neural networks. Before jumping into different deep learning methods, it is crucial to understand why natural language is essential and where it is used.
Where is NLP used?
Since it may be used for language understanding, translation, and invention, NLP has practically endless applications. Chatbots, which can understand questions submitted to them by clients in normal language, is an authentic example of this. These chatbots can determine the purpose and significance of a customer's request and generate spontaneous responses based on the available data.
Even though they are now only utilized as the first line of defense, it shows how deep learning and NLP have extremely real-world applications. Below are some examples of NLP's more typical applications.
1. Automatic text condensing and summarization
Automatic text condensing and summarizing processes shrink a text's size to produce a shorter version. They maintain essential details and eliminate some words or phrases that are either meaningless or do not include details necessary for comprehending the text. This use of natural language processing is helpful when making news digests or bulletins and coming up with headlines. Also, many tools nowadays can summarize any text for you in seconds, like paraphrasingtool.ai.
NLP Summarization vs Conventional Summarization methods
NLP summarizers take the content and condense it into a short version adding new words, breaking sentences into shorter and concluding the information in a paragraph.
Contrarily, traditional summarization techniques compile the top sentences according to word weight and then generate summaries based on the importance of each sentence.
Here is the difference between both:
*Red highlight color represents a different text from the original text.
Source: https://paraphrasingtool.ai/summary-generator/
*Green highlight color represents a similar text as the original text.
2. Language Translation
It should go without saying that converting speech and writing into another language is an arduous task. Each language has its own distinct word patterns and grammatical structures. The word-for-word translation of writings or speech frequently fails because it might alter the underlying tone and meaning.
Natural language processing allows for translating words and phrases into other languages while maintaining the original meaning. These days, Google Translate is driven by Google Neural Machine Translation, which uses machine learning and natural language processing algorithms to recognize various linguistic patterns.
Additionally, machine translation systems are trained to comprehend terms relating to a particular field, such as law, finance, or medicine, for more precise specialized translation.
How different results can be trained on different models?
Let’s compare the three biggest online Machine Translators
English Text
“Additionally, machine translation systems are trained to comprehend terms relating to a particular field, such as law, finance, or medicine, for more precise specialized translation.”
Translated Text
*Red highlight color represents a different text from the original text.
*Green highlight color represents a similar text as the original text.
Back Translation Spanish to English
Google Translator
The back translation made no changes in the original and translated text.
*Green highlight color represents a similar text as the original text.
Bing Translator
No changes were made by Bing Translator when translated back from Spanish to English.
*Green highlight color represents a similar text as the original text.
Yandex Translator
It changed a few words when translated back from Spanish to English.
*Red highlight color represents a different text from the original text.
That’s how Machine Translation models vary from training dataset to output accuracy depending on fine-tuning, databases, and trained languages.
Open Source Language Translation Models
3. Grammar Error Correction
NLP algorithms are able to analyze text and identify patterns in the data. For example, a grammar correction algorithm can be trained to identify errors in sentence structure and suggest corrections. In order to do this, the algorithm first needs to understand the rules of grammar.
This can be done by learning from a corpus of training data that has been manually labeled with correct and incorrect sentences.
Following are the corpus that can be used for training data to make grammar correction application:
Grammarly is one of the popular grammar correctors which uses GECTor and other NLP models.
Advance Techniques Used for Better Text Predictions in NLP
Machines do not directly understand what we write or speak. The human language is first converted into machine-readable chunks. For such purposes, NLP tasks are initiated involving syntactic and semantic analysis.
In syntactic analysis, the relationship between words in a sentence or phrase is identified. This is used to analyze and determine parts of speech in the sentence and the syntactic dominion between them. After that, a syntax tree is formed that portrays the different syntactic classifications of a sentence. It helps in achieving an understanding of a sentence’s syntax.
On the other hand, semantic analysis is the process of understanding the actual meaning based on the semantics of the input text which includes identifying the concepts (relation between them) and finding the entities, like places and things in the text. This process is widely used for developing chatbots, text categorization, and more.
The above explained are the main NLP processes to analyze, predict and translate text. There are some sub-tasks of both syntactic and semantic analysis for text prediction.
Tokenization; Breaking Text
The first on the list is tokenization, which helps break a text into pieces, like words, to help the machine understand the text. However, tokenization doesn’t require machine learning algorithms for English because there is always white space between letters and paragraphs, making tokenizing easy.
In easy words, the paragraphs and sentences in the text are split into smaller units so they can be assigned meaning easily. Here is an example:
Original text: “Eric was helping his father!”
After tokenization: ‘Eric’ ‘was’ ‘helping’ ‘his’ ‘father’
You can see the string is broken into individual parts (each part is called a token) so that a machine understands it.
However, this may seem simple, but this helps a machine understand each part and the whole text. This is important when there is a long string of text because it allows the machine to count and determine certain words’ frequencies and where they appear. This helps in other more complex steps of NLP.
Part-of-speech tagging; Assign parts of speech to each token
As the name suggests, Part-of-speech tagging or PoS tagging is a task where every token from tokenization is assigned with a category. There are many PoS tags like nouns, pronouns, verbs, adjectives, intersections, and others. For this task, using the same example above:
“Eric”: NOUN, “was”: VERB, “helping”: ADVERB, “his”: PRONOUN, “father”: NOUN “!”: PUNCTUATION
As you can see, each of the words is tagged. Thus, doing so helps identify relationships between words and the overall meaning of the paragraph or sentence.
Lemmatization & Stemming
English is a complex language sometimes. When we use inflected forms of a word, it has various grammatical forms and meanings that a computer finds hard to understand. To counter this, NLP uses lemmatization and stemming to get the root form of the words.
Both processes are similar to each other, which reduces inflectional forms, but the methods are not totally the same. The working of both methods is different, which generates different results.
Stemming
Using a list of frequent prefixes and suffixes that can be found in inflected words, stemming algorithms work by removing the beginning or end of the word. We affirm that this strategy has some drawbacks because indiscriminate cutting can be effective occasionally but not always.
Lemmatization
On the other hand, lemmatization is a process that takes the morphological analysis of the words into account. To do this, it is essential to have comprehensive dictionaries that the algorithm may use to connect the form to its lemma.
Here is how it works with the same words as used above.
Stopword Removal
This is another sub-task that is essential in NLP. Stopword removal involves analyzing a sentence or paragraph and filtering out high-frequency words that offer very little to no value. The terms are mostly is, for, at, etc.
The words are removed in a way that it doesn’t change the meaning or topic of the text like if we want to remove stopwords from the phrase “Send me your number, I accidentally deleted it” In this, we can remove “I” “accidentally” “me” “deleted” “it” so you are now left with “send” ““your” “number”. These are now words that still help you understand the topic of the ticket.
Named Entity Recognition (NER)
NLP is used to identify and categorize important entities in the text. NER uses a series of words to detect the entity and then categorize it into a predefined category.
Based on the above statement, it is divided into two steps:
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Detect the entity
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Categorize it accordingly
The following are the basic categories that NER can detect:
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Company
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Person
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Location/place
However, one can also detect the following entities if the model is trained.
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Job Title
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Date
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Email addresses
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Time
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Weight
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Percentage
For instance, if we take a word like IEEE Computer Society, a NER machine can detect and classify it as a “Company”.
The following image shows Google’s NLP model detected 03 entities:
Here is another example and entities extraction from spaCy en_core_web_lg (English)
Example Text:
Michael learned English when he was 8 years old. It was December 1992 when he first got admission in the college.
Entities
NER application is used widely for sorting customer requests, extracting financial leads, and pre-process resumes.
Relationship Extraction with NER
There is another subtask of NLP named relationship extraction. This subtask is another step used to find a relationship between two nouns.
For example in a sentence like “John is from Canada”, a person “John” relates to a place, “Canada” by the semantic category “is from”.
Search engines frequently use this NER to display results that are relevant to the user's search intent.
Source: https://blog.google/products/search/search-language-understanding-bert/
Here is another example of how relationship detection helps search engines show better results:
Source: https://blog.google/products/search/search-language-understanding-bert/
Text Classification
Text classifiers are NLP-based tools for structuring, classifying, and organizing all types of text. On the other hand, sentiment analysis uses sentiments to categorize unstructured data.
Top modeling, language detection, and intent detection are other text classification tasks that can be done with machine learning techniques.
Conclusion
NLP techniques are in practice in many different areas. However, it is a daunting task to empower machines and make sense of natural language to get a novel text. There are so many languages around the world and almost every language obeys a set of rules but still, some exceptions and irregularities are found in these rules.
A simple sentence can have different core meanings when different contexts are taken into account, and there can be meaning in something that is not said and intentional ambiguity.
Such issues in natural languages make teaching machines to understand the language complicated, time-consuming, and labor-intensive. However, deep learning is the key here. It allows a computer to derive meaning or rules from a text on its own.
There are large processors and datasets provided to help them. So, with such abilities, deep learning can help us in many ways, including translation services, content generation, and chatbots.
