#### How to recognize Entities & Intents in traditional NLP system? Are there combined algorithms to recognition both?

- For **Entity recognition**, use open-source libraries like **spaCy** or **Stanford CoreNLP** to implement named entity recognition (NER). These have pre-trained models that can identify common entities like people, organizations, locations, etc.
- For **Intent recognition**, create a dataset of sample user queries labeled with intents. Train a classifier like _logistic regression or SVM_ on this data. Popular open source libraries are scikit-learn and TensorFlow.

Expand the training data with synonyms, varied phrases, and examples to handle diverse user queries. This will improve the accuracy.

RASA uses a combination of approaches:
- Named Entity Recognition(NER) using Conditional Random Fields (CRF) for entity extraction. [NER using CRF](https://medium.com/data-science-in-your-pocket/named-entity-recognition-ner-using-conditional-random-fields-in-nlp-3660df22e95c)
- Recursive Neural Networks for intent classification.

This allows joint modeling of entities and intents in a unified framework.

#### How do you generate response in traditional NLP system?
For response generation:
- **Template-based**: Prepare templates for each intent with slots for entities. Fill the slots and render the template.
- **Retrieval-based**: Have a database of predefined responses. Retrieve the best match for the identified intent.
- **Generative**: Train a **seq2seq** neural network to generate responses from scratch.
- **Hybrid**: Retrieve response templates and fill slots with generative models like **GPT-2**.
- 
For natural conversation flow, maintain dialogue state and context. Refer to previous turns in the conversation when composing the response

Evaluate the system regularly and fix errors. Check intents are correctly identified and responses are relevant. Keep improving the system iteratively.

#### How do you process user chat query in traditional NLP?
- For **spelling correction**, maintain a dictionary lookup to detect misspellings. Use **edit distance algorithms** like Levenshtein distance to find closest word. Popular libraries are **TextBlob, symspellpy, autocorrect**. Integrate them to replace misspellings before intent analysis.
- **Synonym expansion** can be done by using WordNet or word vector similarity. Replace words with common synonyms to handle diverse vocabularies.
- Evaluate regularly by manually checking intents and responses on varied user utterances. Expand training data and tweaking algorithms to handle new utterances.

Overall, a combination of machine learning and rules-based approaches works well. The key is iterative testing and improvement of the NLP pipeline.

