The initial step in every NLP effort should be text preprocessing. Simply stated, preprocessing input text implies transforming the data into a predictable and analyzable format. It’s a critical step in creating a fantastic NLP application.
Text may be preprocessed in many ways, including stop word removal, tokenization, and stemming.
Tokenization is the most crucial of these steps. It is the process of decomposing a stream of textual data into words, phrases, sentences, symbols, or other meaningful components known as tokens. To conduct the tokenization process, a plethora of open-source software are available.
Here are a few things that we will be covering in this blog:
- What is NLP?
- What exactly is tokenization?
- What are the ways by which we can process the data in NLP?
- What tokenizations should one use while solving an NLP task (Types of Tokenization)?
- Word Tokenization
- Character Tokenization
- Subword Tokenization
- Challenges in NLP
What exactly is NLP?
Natural language processing (NLP) refers to a computer program’s capacity to comprehend human language as it is spoken and written – also known as natural language. It’s a part of artificial intelligence (AI).
NLP has been around for over 50 years and has its origins in linguistics. It has a wide range of real-world applications, including medical research, search engines, and corporate intelligence.
Tokenization is one of the least exciting aspects of NLP. How do we divide our text so we can do interesting things with it?
Despite its lack of glitz, it is very essential.
Tokenization determines what our NLP models are capable of expressing. Even while tokenization is critical, it is not always at the forefront of people’s minds.
Ways of Processing Data in NLP
The most popular tokenization algorithm is word tokenization. It divides a block of text into separate words depending on a delimiter. Different word-level tokens are generated depending on the delimiters. Word tokenization includes pre-trained word embeddings such as Word2Vec and GloVe.
Character Tokenization is the process of dividing a piece of text into a set of characters. It solves the disadvantages of Word Tokenization that we discussed before.
Character Tokenizers handle OOV words coherently by retaining the word’s information. It deconstructs the OOV word into characters and expresses it in terms of these characters. It also restricts the vocabulary’s size.
Subword Tokenization divides the text into subwords (or n-gram characters). Lower, for example, may be divided as low-er, smartest as smart-est, and so on.
For vocabulary preparation, transformed-based models — the SOTA in NLP – depend on Subword Tokenization algorithms. Now, let’s take a look at Byte Pair Encoding, which is a common Subword Tokenization method (BPE).
Byte Pair Encoding (BPE) is a popular tokenization technique in transformer-based models. Word and Character Tokenizers are addressed by BPE:
BPE successfully combats OOV. It divides OOV into subwords and expresses the word using these subwords.
When compared to character tokenization, the length of input and output sentences after BPE is shorter.
BPE is a word segmentation method that repeatedly combines the most frequently occurring letter or character sequence. Here is a step-by-step tutorial for learning BPE.
Methods of Processing Data in NLP
1. Named Entity Recognition (NER)
This method is one of the most often used and beneficial in Semantic analysis. Semantics is anything communicated by the text. The algorithm in this method takes a sentence or paragraph as input and detects all nouns or names in that input.
There are numerous common use cases for this method, and we’ve included a few of them here.
This algorithm automatically analyses all news articles and extracts all kinds of information from them, such as persons, businesses, organisations, people, celebrities’ names, and locations. Using this technique, we can quickly categorise news material into various groups.
Efficient Search Engine
The Named entity identification method is used to all articles, results, and news to extract relevant tags and store them separately. These will speed up the search process and provide a more efficient search engine.
On a daily basis, you must have seen hundreds of tweets from individuals complaining about congested places. If the Named Entity Recognition API is utilised, we can simply extract all of the keywords (or tags) and notify the appropriate traffic police agencies.
We learned about the idea previously in this article. Tokenization has two major benefits. The first is that it significantly reduces search, and the second is that it makes efficient use of storage space.
The process of mapping sentences from character to strings and strings into words is the first step in solving any NLP issue since in order to comprehend any text or document, we must first interpret the words/sentences contained in the text.
Tokenization is an essential component of any Information Retrieval (IR) system; it not only pre-processes text but also produces tokens that are utilised in the indexing/ranking process. There are many tokenization methods available, with Porter’s Algorithm being one of the most well-known.
3. Lemmatization and stemming
The growing quantity of data and information on the internet has reached an all-time high in the last few years. This massive amount of data and information necessitates the use of appropriate tools and methods in order to easily draw conclusions.
“The process of reducing inflected (or occasionally derived) words to their word stem, base, or root form – usually a written version of the word” For example, stemming essentially removes all suffixes. So, after applying a stemming step to the word “playing,” it becomes “play,” just as “asked” becomes “ask.”
Lemmatization often refers to actions taken with the appropriate use of vocabulary and morphological analysis of words, with the goal of removing only inflectional ends and returning the base or dictionary form of a word, known as the lemma. Lemmatization, in a nutshell, deals with the lemma of a word, which includes lowering the word form after comprehending the part of speech (POS) or context of the word in any document.
4. Bag of Words
The bag of words method is used to pre-process text and extract all of the characteristics from a text document for use in Machine Learning modelling. It is also a representation of any text that elaborates/explains the occurrence of terms in a corpus (document). It is also known as “Bag” because of its method, which is just concerned with whether recognised words appear in the text, rather than the position of the words.
5. Generating Natural Language
Natural language generation (NLG) is a method for converting raw structured data into plain English (or any other language). We also refer to it as data storytelling. This method is extremely useful in many companies where there is a huge quantity of data; it transforms structured data into natural languages for a better comprehension of patterns or deep insights into any company.
As we have previously discussed, this is the inverse of Natural Language Understanding (NLU). NLG makes data comprehensible to everyone by producing reports that are mostly data-driven, such as stock-market and financial reports, meeting memoranda, product needs reports, and so on.
6. Sentiment Analysis
It is one of the most widely used natural language processing methods. We can understand the emotion/feeling of the written text using sentiment analysis. Emotion AI and Opinion Mining are other terms for sentiment analysis.
The fundamental goal of sentiment analysis is to determine if stated thoughts in any document, phrase, text, social media, or film reviews are positive, negative, or neutral; this is also known as text polarity.
Sentiment analysis is more effective when used to subjective text data rather than objective test results. In general, objective text data are assertions or facts that do not convey any emotion or sentiment. Subjective writing, on the other hand, is often produced by people who express their emotions and sentiments.
7. Sentiment Segmentation
Sentence Segmentation is a term used to describe the process of segmenting a sentence
This technique’s most basic job is to split all material into understandable sentences or phrases. Identifying sentence boundaries between words in text texts is the challenge for this job. Because nearly all languages include punctuation marks at sentence borders, sentence segmentation is also known as sentence boundary detection, sentence boundary disambiguation, or sentence boundary identification.
There are numerous libraries available for sentence segmentation, such as NLTK, Spacy, Stanford CoreNLP, and others, that offer specialised functions to do the job.
NLP Implementation Obstacles
Problems with data
The primary issue is information overload, which makes it difficult to find a particular, essential piece of information among huge databases. Semantic and context understanding is critical for summarisation systems, but it is also difficult owing to quality and usability problems. Identifying the context of interaction among individuals and objects is very critical, particularly when dealing with high-dimensional, diverse, complicated, and low-quality data.
Data ambiguity complicates contextual comprehension even more. Semantics are essential in determining the connection between entities and things. Entities and object extraction from text and visual data cannot give correct information unless the context and semantics of the interaction are recognised. Furthermore, rather than keyword-based search, the presently available search engines may search for things (objects or entities). Semantic search engines are required because they better comprehend user queries, which are often expressed in natural language.
Using Information Extraction
Information Extraction (IE) methods to extract useful and accurate information from unstructured or semi-structured data. It is critical to comprehend the capabilities and limits of current IE methods for data pre-processing, data extraction and transformation, and representations for massive amounts of multidimensional unstructured data. It is critical that these IE systems improve their efficiency and accuracy. However, the dimensionality of data, scalability, distributed computing, flexibility, and usability are difficulties for ML-based methods due to the complexity of large and real-time data. Handling sparse, imbalanced, and high-dimensional datasets effectively is difficult.
Provision for precise details to users
Another issue is that users demand more precise and detailed answers from Relational Databases (RDB) for natural language queries such as English. To obtain information from RDBs for natural language user requests, the requests must be translated into formal database queries such as SQL. They may also make advantage of the existing backend services for the application. This method makes use of natural language processing (NLP) to interpret user queries and generate application service request URLs to obtain data from linked databases.
However, owing to a number of variables, converting NLP questions to formal DB queries or service request URLs is a difficult task in reality. These may be complicated database layouts with table names, columns, restrictions, and so on, or the semantic gap between user language and database nomenclature.
Domain-specific models for intent, context, Named Entity recognition, and extraction are required for NLP search across databases. Text ambiguity, complicated nested entities, identifying contextual information, noise in the form of homonyms, linguistic heterogeneity, and missing data all provide major difficulties in entity detection.
Large reservoirs of textual data are produced from a variety of sources, including web-based text streams and connections through mobile and IoT devices. Despite the fact that ML and NLP have emerged as the most powerful and widely utilised technologies for text analysis, text categorization remains the most popular and widely used method. Text categorization may be either Multi-Level (MLC) or Multi-Class (MC) (MCC). MCC allows just one class label to be assigned to each instance, while MLC allows many labels to be given to a single instance.
Solving MLC issues requires knowledge of multi-label data pre-processing for big data analysis. Due to the features of real-world data, such as high-dimensional label space, label dependence, and uncertainty, drifting, incomplete, and unbalanced, MLC may become extremely complex. Data reduction for high dimensional datasets and multi-instance data classification are equally difficult tasks.
Language Translation Problem
Then there’s the problem of language translation. The primary difficulty in language translation is understanding the meaning of phrases in order to produce an appropriate translation. Each book has unique terms and requires particular linguistic abilities. Choosing the appropriate words for the context and purpose of the material is more difficult.
It is possible that a language does not have a precise match for a certain action or object that exists in another language. Idiomatic phrases describe something using specific instances or figures of speech. Most significantly, the precise meanings of the words included in a sentence cannot anticipate its meaning.
Dealing with new users
Another issue that is somewhat related is the difficulty to properly deal with new users and goods that have no history. Because shops contain numerous items that will not be evaluated by many consumers, the user-item rating matrix is extremely sparse.
The procedure, storage, and maintenance are the usual challenges for any new tools. Building NLP pipelines, as opposed to statistical machine learning, is a complicated process that includes pre-processing, sentence splitting, tokenization, pos tagging, stemming and lemmatisation, and numerical representation of words. To construct models from vast and diverse data sources, NLP necessitates the use of high-end computers.
When compared to statistical ML models, NLP models are bigger and use more memory. Several intermediate and domain-specific models must be kept up to date (e.g. sentence identification, pos tagging, lemmatization, word representation models like TF-IDF, word2vec, etc.). Rebuilding all intermediate NLP models for new data sources may be more expensive.
Although the future of NLP seems to be very difficult and full of dangers, the field is growing at a breakneck speed, and we are on track to achieve a level of progress in the next years that will make complicated applications appear feasible.