Two concepts, one mission: to make machines understand humans. Natural Language Processing (NLP) and Machine Learning (ML) are all the rage right now as techniques that complement each other rather than as NLP vs ML. In this post, we will focus on NLP and how it works together with ML to solve the challenges Artificial Intelligence is posing.
Table of Contents:
- What is Natural Language Processing (NLP)?
- What is Machine Learning (ML)?
- How to Enhance Machine learning through NLP
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is the subfield of computer science able to make computer systems understand human language as humans naturally speak and type. Humans often use language at their wish, most of the time even using abbreviations, misspelling, slang... These variations make it harder for computers to analyze human language. However, NLP and Machine Learning (ML) have lately been making great progress towards solving these issues. Bitext brings a unique approach to the market of Natural Language. As experts in computational linguistics, we are continuously developing new tools designed to enhance NLP and Machine Learning tools, and boost accuracy when machines read and understand human utterances.
Understanding natural language, both with NLP and ML, involves different factors that must be considered:
- Semantics is the branch of linguistics dealing with the meaning of words. A sentence like ‘The bear painted a picture of the landscape’ is incorrect because of the meaning of the verb ‘to paint’, an action that must be done by a human being. To realize that the sentence makes no sense, one must know the real definition of the word ‘paint’ here.
- Syntax. Text and sentence structure are important to understand text meaning. Given a sentence such as, ‘Laura joined the team having some experience. Who exactly has the experience? Laura, or the team? Laura’s abilities are determined here depending on the reader’s mind.
- Context. The last two factors are essential to get a good understanding, but sometimes there are some external aspects you must understand so you know what a text is talking about. If someone says, ‘that’s wicked!’, is there a positive or a negative meaning behind it? One must know about the context in which it was uttered to get it.
Let’s see everything together in the sentence mentioned above: ‘The bear painted a picture of the landscape’:
- Semantics: animal – the act of to represent by or as if by a picture – a design or representation made by various means – a portion of territory.
- Syntax: subject – action – direct object.
- Context: this sentence is about a bear painting a picture.
These three on their own are not enough, but a combination of them is what gives a full language understanding of the sentence.
Natural Language Processing (NLP) and Machine Learning(ML) techniques are evolving and these issues are being addressed from both angles. At Bitext, we use a hybrid approach which greatly increases the accuracy of the results.
What is Machine Learning (ML)?
When talking about text analytics, machine learning is considered a combination of statistical techniques which serves to detect patterns including sentiment, entities, parts of speech and other phenomena within a text. There are two kinds of machine learning procedures: supervised and unsupervised. Supervised machine learning is the process where the ML techniques can be expressed as a model which can be also applied to other texts. There are also some algorithms working across extensive data sets to obtain meaning, also known as unsupervised machine learning. Knowing the differences between supervised and unsupervised learning and how to get the best of both in one system is essential to get the best results:
- Supervised Machine Learning- In supervised machine learning, there is a big amount of manually-tagged documents to find patterns in a text. This set of tagged documents can be used to train statistical models to be applied afterward to new texts. The bigger the data set, the better results: every model can be trained multiple times to enhance its learning.
- Unsupervised Machine Learning- The statistical models used to extract meaning from texts in unsupervised machine learning don’t require any pre-tagged sets, as seen before. ‘Clustering’ is considered one of these unsupervised ML techniques: the act of gathering documents together into groups through a hierarchical relationship. There is another unsupervised technique known as ‘latent semantic indexing’. This technique can be used for multifaceted document search: if the words ‘TV’ and ‘channel’ are related in many texts, then you’ll very likely get documents back containing ‘channel’, even if you just search for ‘TV’.
How to Enhance Machine learning through NLP
- Tokenization- Tokenization task is to identify the basic relevant components of sentence, words or parts of words. In English, for instance, it can be considered a relatively easy task due to the spaces between words. However, in Mandarin Chinese – where there are no spaces – an adequate algorithm and high-quality dictionaries are crucial to identify words.
- POS-Tagging-Part-of-Speech Tagging is a good example of how NLP and Machine learning (ML) complement each other, it is used for several NLP tasks such as topic or entity extraction. At Bitext, our NLP models are built to tag ‘parts of speech’ with up to 90% accuracy, even for slang and language variants used in social media.
- Entity Extraction- This is another good example of integration con NLP and ML. Our natural language processing model, used for the extraction of named entities in a text, is able to recognize up to 15 different entities: people, places, phone numbers, email addresses, companies, URLs, money, Twitter users…
- Topic-Based Sentiment Analysis- This is one of the biggest challenges for AI and text analysis. Identifying sentiment and its topic poses challenges for which we need to combine NLP and ML techniques. Bitext Sentiment Analysis tool is based on topics and identifies opinions and emotions regardless of the source: surveys, reviews, searches, conversations, etc. It analyzes opinions found in texts to detect an emotional response from users in more than 20 languages. First, it identifies the topic(s) that are being discussed in a particular text and then evaluates the opinion(s) expressed about the topic(s) and its polarity (positive/negative).
- Categorization- This problem is typically treated using ML techniques, given the diversity of categorization tasks. However, symbolic approaches can provide effective solutions for very specific tasks. Our categorization service classifies texts into groups according to customized categories. Our team of computational linguists create rules based on linguistic analysis so that the accuracy reached is more stable. Take a look at our case study for the automotive industry to get a better idea.