NLP Tasks

NLP -> Natural language Processing

• Classification

• Multi Classification

• Generation

Classification

Input will be simple text, and output would be predict something

Sentiment Extraction

Ex: This teddy bear is so cute

Datasets : amazon, IMDB, Twitter

Evaluation Metrics:

Accuracy -> % of observation that were correctly predicted

Precision -> % of predicted positive that were correct

Recall -> % of actually positive that were correct

F1 Score -> Score that is a function of precision and recall - harmonic mean

why do we need metrics -> to analyze the different datasets.

Intent detection

Language detection

Topic Modelling

Multi Classification

Output would be multiple predictions

Ex: Part of speech tagging

Named entity recognition:

Identify the different named entities from the input text

Ex: A cute teddy bear is reading

Teddy Bear -> entity

Datasets: Annotated reuters news paper (CoNLL - 2003, CoNLL++)

Dependency parsing

Constituency parsing

Generation

Input as text and output also as text and dont know what is the length of the output

Machine translation

Datasets : WMT'14 English French, WMT'14 English German

Evaluation Metrics:

BLEU -> Bi lingual evaluation under study -> quality of text translated

ROGUE -> Quality of text generated

Perflexity -> Quantifies how 'surprised' the model is to see some words together

Question Answering

Summarization

Text generation