What is Natural Language Processing?
NLP that stands for Natural Language Processing can be defined as a subfield of Artificial Intelligence research. It is completely focused on the development of models and protocols that will help you in interacting with computers based on natural language. Text and speech-based systems are included in this.
As we all know that human language is very complicated by nature, the building of any algorithm that will human language seems like a difficult task, especially for the beginners. It’s a fact that for the building of advanced NLP algorithms and features a lot of inter-disciplinary knowledge is required that will make NLP very similar to the most complicated subfields of Artificial Intelligence.
In this article, I’ve compiled a list of the top 15 most popular NLP algorithms that you can use when you start Natural Language Processing.
Lemmatization and Stemming
Lemmatization and Stemming are two of the techniques that help us create a Natural Language Processing of the tasks. It works well with many other morphological variants of a particular word.
These techniques let you reduce the variability of a single word to a single root. For example, we can reduce “singer”, “singing”, “sang”, “sung” to a singular form of a word that is “sing”. When we do this to all the words of a document or a text, we are easily able to decrease the data space required and create more enhancing and stable NLP algorithms.
Lemmatization and Stemming are thus, pre-processing techniques, which means that we can use one of the two NLP algorithms according to our needs before we go forward with the NLP project so that we can create up data space for more data and prepare the databank.
Lemmatization and Stemming are two very different techniques and both of them can be completed using various other ways, but the ultimate result is the same for both: a smaller search space for the problem we are facing.
One of the most important tasks of Natural Language Processing is Keywords Extraction which is responsible for finding out different ways of extracting an important set of words and phrases from a collection of texts. All of this is done to summarize and help to organize, store, search, and retrieve contents in a relevant and well-organized manner.
There is a large number of keywords extraction algorithms that are available and each algorithm applies a distinct set of principal and theoretical approaches towards this type of problem. We have different types of NLP algorithms in which some algorithms extract only words and there are one’s which extract both words and phrases. We also have NLP algorithms that only focus on extracting one text and algorithms that extract keywords based on the entire content of the texts.
Here are some most popular keywords extraction algorithms discussed below:
- TextRank: this works on the same principle behind the PageRank algorithms. Through which Google assigns significance to various web pages on the internet.
- TF-IDF: Full form of TF-IDF is Term Frequency – Inverse Document Frequency which aims on defining how significant a word is a document in a better way. Also considering the relation between other documents from the same corpus.
- RAKE: Rapid Automatic Keywords Extraction are fallen under the category of NLP algorithms. This can extract keywords and key phrases based upon the text of one document, without considering other documents in the same collection.
Topic Modeling is an NLP activity where we strive to identify “abstract subjects” that can define a text set. This suggests that we have a set of texts and we strive to identify word and expression trends that can help us organize the documents and classify them by “topics.”
Latent Dirichlet Allocation is one of the most common NLP algorithms for Topic Modeling. You need to create a predefined number of topics to which your set of documents can be applied for this algorithm to operate.
At first, you allocate a text to a random subject in your dataset and then you go through the sample many times, refine the concept and reassign documents to various topics.
Two figures calculate this by:
- The possibility that a specific document refers to a particular term; this is dependent on how many words (except the actual word) from that document belong to the current term.
- The proportion of documentation allocated to the context of the current term is given the current term.
A method of storing information utilizing triples is described by knowledge graphs-a collection of three items: a subject, a predicate, and an entity.
Awareness graphs belong to the field of methods for extracting knowledge-getting organized information from unstructured documents.
Knowledge graphs have become increasingly common recently, notably when they are used by several businesses (such as the Google Information Graph) for different goods and services. Building a knowledge graph involves a wide range of NLP techniques (maybe every technique listed in this article) and using more of these techniques will probably help you develop a more detailed and effective knowledge
Named Entity Recognition
Name Entity Recognition is another very important technique for the processing of natural language space. It is responsible for defining and assigning people in an unstructured text to a list of predefined categories. This includes people, groups, times, money, and so on.
Named Entity Recognition consists of two sub-steps. These steps include Named Entity Identification (identification of potential NER algorithm candidates) and Named Entity Classification (assignment of candidates to one of the pre-defined categories)
A word cloud or tag cloud represents a technique for visualizing data. Words from a document are shown in a table, with the most important words being written in larger fonts, while less important words are depicted or not shown at all with smaller fonts.
To explain our results, we can use word clouds before adding other NLP algorithms to our dataset.
Machine Translation is a classic exam for understanding language. It consists of both linguistic study and the development of languages. Big computer translation technologies make tremendous industrial use, as the global language is a $40 trillion market each year. To give you a few striking examples:
- Google Translate holds 100 billion words a day.
- Facebook uses machine translation to automatically translate text into posts and comments, to crack language barriers. It also allows users around the world to communicate with each other.
- To allow cross-border trading and connect buyers and sellers around the world, eBay uses Machine Translation Software to.
- On Linux, iOS, and Amazon Fire, Microsoft introduces AI-powered translation to end-users and developers, whether they have access to the Internet or not.
- Back in 2016 Systran became the first tech provider to launch a Neural Machine Translation application in over 30 languages.
In a typical method of machine translation, we may use a concurrent corpus — a set of documents. Each of which is translated into one or more languages other than the original. For eg, we need to construct several mathematical models, including a probabilistic method using the Bayesian law. Then a translation, given the source language f (e.g. French) and the target language e (e.g. English), trained on the parallel corpus, and a language model p(e) trained on the English-only corpus.
Needless to mention, this approach skips hundreds of crucial data, involves a lot of human function engineering. This consists of a lot of separate and distinct machine learning concerns and is a very complex framework in general.
Dialogue and Conversations
Much has been published about conversational AI, and the bulk of it focuses on vertical chatbots, communication networks, industry patterns, and start-up opportunities (think Amazon Alexa, Apple Siri, Facebook M, Google Assistant, Microsoft Cortana). The capacity of AI to understand natural speech is still limited. The development of fully-automated, open-domain conversational assistants has therefore remained an open challenge. Nevertheless, the work shown below offers outstanding starting points for individuals. This is done for those people who wish to pursue the next step in AI communication.
A Recurrent Neural Network architecture is used to resolve the problems of sparsity that occur as contextual knowledge is inserted into classical mathematical models, enabling prior dialogue utterances to be taken into account by the system. Over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines, the model reveals clear gains.
Neural Responding Machine (NRM) is an answer generator for short-text interaction based on the neural network. It requires the general structure for encoder-decoder. Second, it formalizes response generation as a decoding method based on the input text’s latent representation, whereas Recurrent Neural Networks realizes both encoding and decoding.
With a large amount of one-round interaction data obtained from a microblogging program, the NRM is educated. Empirical study reveals that NRM can produce grammatically correct and content-wise responses to over 75 percent of the input text, outperforming state of the art in the same environment.
Sentiment analysis is the most commonly used method in NLP. Analysis of the emotions is most helpful in situations such as consumer polls, ratings, and discussions on social media where users share their thoughts and suggestions. A 3-point scale is the easiest production in emotion analysis: positive/negative/neutral. The production can be a statistical score in more complex instances that can be bucketed into as many categories as required.
Sentiment Analysis can be performed using both supervised and unsupervised methods. Naive Bayes is the most common controlled model used for an interpretation of sentiments. A training corpus with sentiment labels is required, on which a model is trained and then used to define the sentiment. Naive Bayes isn’t the only platform out there-it can also use multiple machine learning methods such as random forest or gradient boosting.
Often known as the lexicon-based approaches, the unsupervised techniques involve a corpus of terms with their corresponding meaning and polarity. The sentence sentiment score is measured using the polarities of the express terms.
There are techniques in NLP, as the name implies, that help summarises large chunks of text. In conditions such as news stories and research articles, text summarization is primarily used.
Extraction and abstraction are two wide approaches to text summarization. Methods of extraction establish a rundown by removing fragments from the text. By creating fresh text that conveys the crux of the original text, abstraction strategies produce summaries. For text summarization, such as LexRank, TextRank, and Latent Semantic Analysis, different NLP algorithms can be used. This algorithm ranks the sentences using similarities between them, to take the example of LexRank. A sentence is rated higher because more sentences are identical, and those sentences are identical to other sentences in turn.
The sample text is summarised using LexRank as: I must call the call center several times before I get a reasonable response.
The numerous facets in the text are defined by Aspect mining. It removes comprehensive information from the text when used in combination with sentiment analysis. Part-of – speech marking is one of the simplest methods of product mining.
When aspect mining is used on the sample text along with sentiment analysis. The production conveys the full purpose of the text:
- Aspects & Sentiments:
- Customer service – negative
- Call center – negative
- Agent – negative
- Pricing/Premium – positive
One of the more complex approaches for defining natural topics in the text is subject modeling. A key benefit of subject modeling is that it is a method that is not supervised. There is no need for model testing and a named test dataset.
There are quite a few modeling algorithms for the topic:
- Latent Semantic Analysis (LSA)
- Probabilistic Latent Semantic Analysis (PLSA)
- Latent Dirichlet Allocation (LDA)
- Correlated Topic Model (CTM)
The latent Dirichlet allocation is one of the most common methods. The LDA presumes that each text document consists of several subjects and that each subject consists of several words. The input LDA requires is merely the text documents and the number of topics it intends.
The subject modeling performance would classify the common terms in both topics by using the sample text and assuming two implicit topics. The key theme for the first topic 1 involves terms such as call, core, and service for our example. In topic 2, the main focus is termed such as premium, fair, and price. This assumes that subject 1 corresponds to customer service and subject two corresponds to the price
BAG OF WORDS
A text is represented as a bag (multiset) of words in this model (hence its name), ignoring grammar and even word order, but retaining multiplicity. The bag of words paradigm essentially produces a matrix of incidence. Then these word frequencies or instances are used as features for a classifier training.
Sadly, there are many downsides to this model. The worst is the lack of semantic meaning and context and the fact that such words are not weighted accordingly (for example, the word “universe” weighs less than the word “they” in this model).
It’s the mechanism by which text is segmented into sentences and phrases. Essentially, the job is to break a text into smaller bits (called tokens) while tossing away certain characters, such as punctuation.
Text input: yesterday Peter walked to school.
Text output: Peter, went to, yesterday, school
The biggest drawback to this approach is that it fits better for certain languages, and with others, even worse. This is the case, especially when it comes to tonal languages, such as Mandarin or Vietnamese. The Mandarin word ma, for example, may mean “a horse,” “hemp,” “a scold” or “a mother” depending on the sound. The NLP algorithms face a real threat.
STOP WORDS REMOVAL
For eg, the stop words are “and,” “the” or “an” This technique is based on the removal of words which give the NLP algorithm little to no meaning. They are called stop words, and before they are read, they are deleted from the text.
This approach has a few disadvantages to it:
- The database of the NLP algorithm is not riddled with terms which are not useful
- The Text is interpreted quicker
- The algorithm can be trained more quickly because the training set contains only vital information
Naturally, there are also downsides. There is always a risk that the stop word removal can wipe out relevant information and modify the context in a given sentence. That’s why it’s immensely important to carefully select the stop words, and exclude ones that can change the meaning of a word (like, for example, “not”).
In this article, we took a look at some quick introductions to some of the most beginner-friendly Natural Language Processing or NLP algorithms and techniques. I hope this article helped you in some way to figure out where to start from if you want to study Natural Language Processing. You can also check out our article on Data Compression Algorithms.
Thank you for reading this article. I hope this article was useful to you.