A Brief Analysis of NLP Technology

NLP breaks the language into shorter segments to understand the relationship between the segments and how the segments are combined. There are two language components: syntax (words are arranged in a sentence according to their grammatical sense) and semantics (the meaning conveyed by the text). Each category has core NLP technologies.

Syntax analysis

Here are some standard methods used by machines to analyze syntax:

Segmentation: Break a sentence into smaller segments

Lemmatization: narrow a word to the base, and combine words with similar grounds together

Part of speech tagging: indicates the property of each word

Extract stems: delete the prefix and suffix of the word to get the root

Semantic analysis

Here are two popular methods used by machines to analyze semantics:

• Named entity recognition: Identify preset groups (such as people and places) and classify them

• Word sense disambiguation: Determine the word sense according to the context

It should be noted that computers do not think in the same way as humans do; they only think logically. The complexity of natural language should not be underestimated. Humans express themselves in countless ways. There are hundreds of languages ​​and dialects all over the world, and each language, whether written or spoken, has its own grammatical rules and slang, and each is different. For a computer to understand all these differences, it has to encounter these differences beforehand. Moreover, it has to be trained based on similar data. Another challenge is that the training database should belong to the same domain as the intended application.

Introduction to text emotional annotation

Emotion tagging evaluates the attitudes and emotions in the text, labeling the text as positive, negative, or neutral.

More reading: Sentiment analysis

Text sentiment analysis, also known as opinion mining, tendency analysis, etc. In a sentence, it is the process of analyzing, processing, summarizing, and reasoning. On the Internet (such as blogs, forums, and social service networks), many users make valuable comments such as characters, events, and products. These messages express people’s various emotional attitudes and tendencies, such as joy, anger, sorrow, joy and criticism, praise, etc. Based on these, potential users can understand the public opinion on a certain event or product by browsing these comments.

Most sentiment analysis research focuses on explicit emotions because such emotions are easier to discover and analyze. There are usually two aspects to analyzing sentiment:

• Emotional polarity: analyze the emotions(Is it positive or negative?)

• Emotional intensity: the degree of affection from high to low

Need to develop project charter and standards

Make text-based emotion labeling easier. Many sentiment analysis projects involve a large number of text annotations. Straightforward explicit texts like “coffee tastes awful” can require annotators to label “positive,” “negative directly,” or neutral. Complex implicit text is challenging to develop a standard. Therefore, standards are significant when expressing difficult emotions such as “sarcasm” and “irony,” which directly affect the project cycle and the data quality.

High demand for scalable and customized dataset

At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, finance, etc. Each field has requirements for subdivision and specialization.

Among them, in particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.

In the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.

What we need to be clear is for AI companies and the entire industry, data annotation is an important part of the realization of artificial intelligence. The accuracy and efficiency of the labeled data affect the final result of the artificial intelligence algorithm model.

ByteBridge, a human-powered and ML-powered data labeling tooling platform

ByteBridge is a data labeling SaaS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.

Accuracy

• ML-assisted capacity can help reduce human errors by automatically pre-labeling
• The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
• Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
• All results are thoroughly assessed and verified by a human workforce and machine

ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

Cost-effective

A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.

NLP Service

We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).

Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.

End

If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.