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.<\/p>\n
One example of this is with determining customer satisfaction. Values of a customer\u2019s 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\u2019s experience, their levels of loyalty, and the business\u2019 overall bottom line.<\/p>\n
Common techniques used in text annotation for machine learning<\/h2>\n
Some of the most common techniques or NLP annotation tools used in text annotation for machine learning include the following:<\/p>\n
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- POS tagging<\/strong>: POS tagging is also known as part of speech tagging. This means that a sentence\u2019s words in a given language will be allocated a tag depending on the part of speech of each word in the sentence.<\/li>\n
- NER or named entity recognition annotation<\/strong>: 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.<\/li>\n
- Dependency parsing<\/strong>: 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.<\/li>\n
- Sentiment analysis<\/strong>: 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.<\/li>\n
- Topic modeling<\/strong>: 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.\u00a0<\/li>\n<\/ul>\n
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.\u00a0<\/p>\n
Is NLP annotation difficult in Vietnamese?<\/h2>\n
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.<\/p>\n
Exploring Language Annotation and NLP in Vietnamese<\/h2>\n
Whichever NLP labeling tool or annotation tool you choose to use, it\u2019s 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%.<\/p>\n
- NER or named entity recognition annotation<\/strong>: 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.<\/li>\n