NLP

- Implement sentence tokenization and Named Entity Recognition

To implement sentence tokenization and Named Entity Recognition, you can use libraries like NLTK or spaCy. Tokenization can be achieved using functions like nltk.sent_tokenize() for sentences and nltk.word_tokenize() for words. For Named Entity Recognition (NER), Conditional Random Fields (CRFs) can be used to identify entities such as names, places, and organizations.

- How can you tokenize a paragraph into sentences and then further into words using NLTK?

To tokenize a paragraph into sentences using NLTK, you can use the sent_tokenize() function. For further splitting sentences into words, the word_tokenize() function can be used. Example code would look like:

1import nltk
2sentences = nltk.sent_tokenize(paragraph)
3words = [nltk.word_tokenize(sentence) for sentence in sentences]

- What is stemming and lemmatization, and when should each be used?

Stemming and lemmatization are techniques used to reduce words to their base or root form. Stemming is generally faster but less accurate, and it may produce non-real words. Lemmatization is slower but more accurate, and it produces real words. Stemming is suitable for quick prototyping, while lemmatization should be used when the quality of NLP is crucial.

- What are word embeddings and why are they important?

Word embeddings are vector representations of words that capture their meanings, syntactic and semantic relationships. They are crucial in NLP because they allow models to understand and interpret text data more effectively, enabling better performance in tasks like classification, translation, and sentiment analysis.

- How do you handle imbalanced text data in classification tasks?

When dealing with imbalanced text data in classification tasks, techniques like oversampling the minority class, undersampling the majority class, or using synthetic data generation methods like SMOTE can be applied. Another approach is to use different evaluation metrics such as F1-score, precision, and recall instead of accuracy.

- What is Topic Modeling and what are its applications?

Topic Modeling is a technique used to automatically identify topics present in a text corpus. Algorithms like Latent Dirichlet Allocation (LDA) are commonly used for this. Applications include document classification, recommendation systems, and content summarization.

- How can you measure the similarity between two text documents?

Text similarity can be measured using various techniques such as Cosine Similarity, Jaccard Similarity, or using advanced methods like Word2Vec or Doc2Vec models. These metrics can help in applications like document retrieval, clustering, and deduplication.

- What is the role of Attention Mechanisms in NLP?

Attention Mechanisms help models focus on specific parts of the input text, much like how humans pay attention to specific portions when reading or listening. This is particularly useful in tasks like machine translation and text summarization where the context is crucial.

- Explain the concept of Transformer models in NLP.

Transformer models, introduced in the paper "Attention Is All You Need," are a type of neural network architecture that relies solely on attention mechanisms. They have been highly effective in a wide range of NLP tasks and are the basis for models like BERT, GPT, and T5.