RESEARCH TRENDS IN NLP
What is NLP ?
Natural Language Processing or NLP is visualized as a natural manipulation of common languages, such as speech and text, by software. It facilitates the perfect connection between human language and computer language.
- Sentiment analysis
Sentiment Analysis is one of the methods of processing Natural Language, which can be used to determine the background sensitivity of texts. The purpose of Emotional Analysis is only to predict the sentiments of the text. Using natural language processing tools to assess product sentiment can help businesses identify areas for improvement, gain negative feedback on social media, and gain competitive advantage.
2. Collaboration of supervised and Unsupervised learning
When supervised learning and non-supervised learning are used together, giving NLP the next level of strength and ways to improve the NLP model seems to produce very accurate results. Supervised learning, which is often used for tasks like topic categorization, necessitates a significant quantity of labeled data as well as several repetitions before a model can produce good predictions. No data is labeled for unsupervised: the model learns in input data and can detect patterns and reach conclusions in unseen data on its own. Combining supervised and supervised reading has been proven to improve the performance of machine learning models, especially text analysis.
3. low code tool
Improving NLP models requires an advanced understanding of AI and ML code but low codes or no coding tools introduced, SaaS companies such as MonkeyLearn aim to democratize NLP and machine learning technology, allowing non-technical users to perform tasks. NLP which was accessible only to them. data scientists and developers.
The builder of the MonkeyLearn point model and click make it easy to build, train, and integrate text editing models or sentiment analysis models in just a few steps, which means we can expect to see more businesses using NLP tools.
4. Predict Closed Questions in StackOverflow
An NLP building model that predicts whether a question will be closed or not in view of the question as it has been submitted. . Predict whether a new question or post will be closed or not, and predict the reason for closing the post.
5. StumbleUpon Evergreen Classification
Web classification is a very important problem of machine learning and extensive use in tasks such as news classification, content prioritization, sentiment analysis and more, and a given method works to solve stumbleupon evergreen classification by focusing primarily on developing a predictive model using machine learning strategies or techniques for one such problem that classifies if web posting is everlasting . , known as evergreen. Finally used an advanced model in a different web classification problem.
6. Avito Duplicate Ads Discovery
Avito is a Russian classified ad site with categories dedicated to general merchandise, jobs, property, personal, automotive products and services. The purpose of this challenge is to develop a model that can automatically detect repeat ads more accurately. It will help Avito to make their platform easier for buyers and makes their next purchase with an honest seller.
7. Similarities of Quora Questions
Quora is a platform for queries and interactions with people who provide unique information and quality answers. The data set for this challenge has features that include two queries and their unique ids. The target class label is ‘is _duplicate’, which means whether the two questions are duplicate or not. You can use NLP techniques to produce two text elements and train a model in it to distinguish whether the questions are duplicate or not.
8. Classification of Toxic Comments
The goal is to create a classification model that can predict whether the included text is inappropriate or toxic. If a series of sentences or phrases are used as user comments on an online forum, classify them as part of categories such as toxins, severe toxicity, obscenities, threats, insults, self-esteem, hate with different values or approximate and develop a model that predicts the probability for each type of toxin in each comment.
9. Keyword Domain — StackOverflow Tag Prediction
This challenge is organized by Facebook to recruit top data science players. The purpose of the challenge is to identify keywords and tags in millions of text queries. This casestudy is the problem of separating multiple labels, where multiple tags need to predict a particular text, when each question mark on the overflow website stack label and their prediction actually does not differentiate.
10. Two Sigma Connect: Rental Listing Questions
The contest is organized by two sigma, and the data set for this competition comes from the apartment listing website — renthop. Com. The purpose of this competition is to predict how much interest you will earn on a new rental list on renthop. The forecast will be based on listed content such as text description, photos, bedroom number, price etc.
To conclude, NLP is the relationship between human language and computer language. In this blog, we have read about the latest trends in NLP. The trends in the field of NLP itself will gain popularity among developers.
This trends are achieving recognition, it can be easily expected that in the coming year natural language processing is going to be more popular and in the best position in the technology market.
Written by : Astitva Ghanmode , Shruti Nikam , Omkar Dalwai , Swati chim , Pratik Waso
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