Reference Links:
-
How to handle Outliers in the text data - Refer to Chapter 6 https://link.springer.com/content/pdf/10.1007/978-1-4614-6396-2_7.pdf
-
Outlier detection in Medical Claims http://sujitpal.blogspot.com/2014/05/outlier-detection-on-medical-claims.html
-
Text document clustering https://stats.stackexchange.com/questions/171469/outliers-in-text-document-clustering
-
Outlier detection using One-Class SVM and PCA - Use case Credit Card Fraud Detection https://gallery.cortanaintelligence.com/Experiment/1219e87f8fb84e88a2e1b54256808bb3
-
Anomalies Detection in Evaluation of Essay Type Answers by Multiple Evaluators http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6654413
-
Outlier Detection for Text Data : An Extended Version https://arxiv.org/pdf/1701.01325.pdf
-
Outlier detection in financial statements: a text mining method https://www.witpress.com/Secure/elibrary/papers/DATA09/DATA09008FU1.pdf
Some of the major tasks that are a part of NLP include:
- Automatic summarization
- Coreference resolution
- Discourse analysis
- Machine translation
- Morphological segmentation
- Named entity recognition (NER)
- Natural language generation
- Natural language understanding
- Optical character recognition (OCR)
- Part-of-speech tagging
- Parsing
- Question answering
- Relationship extraction
- Sentence breaking (also known as sentence boundary disambiguation)
- Sentiment analysis
- Speech recognition
- Speech segmentation
- Topic segmentation and recognition
- Word segmentation
- Word sense disambiguation
- Lemmatization
- Native-language identification
- Stemming
- Text simplification
- Text-to-speech
- Text-proofing
- Natural language search
- Query expansion
- Automated essay scoring
- Truecasing