Language annotation has been a part of linguistics for many decades now, going back as far as the 1950s and even earlier. And with the continued development of technology such as computers, statistical modeling, and artificial intelligence (AI), language annotation has gained ever-increasing prominence in the field of language processing and translation.
If language annotation and natural language processing annotation (NLP annotation) are new concepts to you and you’d like to get a better understanding of them, especially in the context of the Vietnamese language, this article is for you.
What is language annotation?
Firstly, we ask the question – what is language annotation? We can begin by breaking up the concept into two parts. Let’s start with the word “annotation”. It essentially means to make notes on a given text. With this in mind, language annotation is the process of making notes on a particular language. But these notes are not general or subject-specific.
Instead, they are notes that put a value or a token on a certain word in a sentence so that a greater body of data can be collected about the language. In turn, this is used in NLP annotation, which we cover in more detail below.
What about NLP annotation?
If language annotation is assigning values to language, then NLP annotation takes the process further. For example, a body of language and the words that it is made up of is assigned a value or a token depending on a particular word’s positioning, function, and use in a sentence. With this in mind, this body of language and its related tokenizations constitute a language corpus.
This corpus is the foundation of the metadata that is fed into machine learning (ML) and is consequently called ML annotation. It must be noted that NLP annotation is a part of AI and ML and aims to take a broad body of text (and even speech) and create accurate language translations from a source language into a target language.
Therefore, if language annotation is the process of allocating certain values and functions to a specific language, then NLP annotation takes the process further and feeds this data into smart machines or computers to try and get the highest possible statistically relevant output for that language.
Where is this service needed?
Language annotation, NLP annotation, and ML annotation are used in a variety of industries today. Essentially, anywhere where large volumes of data, text, or speech are processed on a regular basis. Examples of instances where these types of annotation can be used include:
- Chatbots
- Call centers
- Linguistic services
- Data processing
- E-commerce
- And many others.
One of the reasons behind the broad reach of language annotation and NLP annotation is the fact that borders across the world are shrinking. Businesses are expanding across geographical boundaries and need to process customer data, information, requests, questions, and inquiries in a target language from a source language quickly and efficiently. In addition to this, although it is still hard for many computational models to analyze emotions, sentiment analysis can come into play with NLP annotation as certain values are assigned to a customer experience.
One example of this is with determining customer satisfaction. Values of a customer’s experience with an organization may be assigned as follows: positive, neutral, or negative. Based on this, computers, chatbots, and humans can choose the right course of action to modify and improve the customer experience and therefore inadvertently affect the customer’s experience, their levels of loyalty, and the business’ overall bottom line.
Common techniques used in text annotation for machine learning
Some of the most common techniques or NLP annotation tools used in text annotation for machine learning include the following:
- POS tagging: POS tagging is also known as part of speech tagging. This means that a sentence’s words in a given language will be allocated a tag depending on the part of speech of each word in the sentence.
- NER or named entity recognition annotation: named entity recognition annotation refers to literally naming entities such as people, places and locations, and organizations and mapping these within a wider linguistic context.
- Dependency parsing: in this technique, the grammatical structure of a sentence is analyzed in depth to determine the relationship between the words in the sentence as well as their relevance in creating structured meaning.
- Sentiment analysis: with sentiment analysis, the aim is to determine the sentiment of a user by trying to understand the emotions behind the language used. As mentioned earlier, this can be highly challenging for machines to achieve but it is possible to study the language used by a customer and allocate an emotional value to it.
- Topic modeling: finally, topic modeling is a time-saving exercise where certain critical words are extracted from a wider corpus to provide greater levels of meaning and understanding.
These are just some of the NLP annotation tools, NLP labeling tools, and techniques that give language greater meaning, context, and clarity when it comes to processing language by machines.
Is NLP annotation difficult in Vietnamese?
Vietnamese is considered an isolating language with no word delimiters. These are two of the main reasons why there is no associated large corpus of language data available and what makes NLP annotation difficult in Vietnamese. Nevertheless, numerous researchers are attempting to overcome this stumbling block by building treebanks and using various other models in an attempt to build the language corpus and make it more easily processable by NLP and ML with greater accuracy.
Exploring Language Annotation and NLP in Vietnamese
Whichever NLP labeling tool or annotation tool you choose to use, it’s critical to understand their role and purpose. With language and NLP annotation, we must build a corpus for NLP and ML to ensure greater consistency of results for Vietnamese, which is considered a language with lower corpus data. Despite progress being made in this regard, more needs to be done to boost the accuracy of language annotations for Vietnamese and attempt to reach results over and above the current success rates in the region of 92%.