Contents
How can you increase the accuracy of NLP?
8 Methods to Boost the Accuracy of a Model
- Add more data. Having more data is always a good idea.
- Treat missing and Outlier values.
- Feature Engineering.
- Feature Selection.
- Multiple algorithms.
- Algorithm Tuning.
- Ensemble methods.
What is the latest NLP model?
10 Leading Language Models For NLP In 2021
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
- GPT2: Language Models Are Unsupervised Multitask Learners.
- XLNet: Generalized Autoregressive Pretraining for Language Understanding.
- RoBERTa: A Robustly Optimized BERT Pretraining Approach.
Why are stop words important in text preprocessing?
Often, stop words such as those are the most frequent words in any slice of text, and they are so because they form the functional skeleton of any sentence that communicates the grammatical relationships that the content materials has. Many NLP libraries come equipped with lists of stop words, but there is no fixed definition of a stop word.
Which is the best way to preprocess text?
There are different ways to preprocess your text. Here are some of the approaches that you should know about and I will try to highlight the importance of each. Lowercasing ALL your text data, although commonly overlooked, is one of the simplest and most effective form of text preprocessing.
How to improve accuracy of OCR using image preprocessing?
Improve Accuracy of OCR using Image Preprocessing 1 Scaling of image : Image Rescaling is important for image analysis. 2 Skew Correction : A Skewed image is defined as a document image which is not straight. 3 Binarization : Mostly, an OCR engine does binarization internally because they work on Black & White images.
How to use image preprocessing to improve accuracy of tesseract?
We’ve got two more parameters that determine the size of the neighborhood area and the constant value that is subtracted from the result: the fifth and sixth parameters, respectively. 3. Otsu’s Threshold This method particularly works well with bimodal images, which is an image whose histogram has two peaks.