Text Preprocessing and Tokenization Quiz

HMM is used in NLP to capture dependencies between words by determining the transition and emission probabilities of different parts of speech and word meanings.

NER is the identification and classification of named entities such as person, organization, place, date, and time, while chunking involves dividing sentences into syntactically meaningful parts.

The typical types of named entities recognized in NER are person, organization, place, date, and time.

<p>These models capture complex semantic nuances by analyzing the contextual relationships between words and understanding the meaning and usage of words in different contexts.</p> Signup and view all the answers

<p>Chunking is used to divide sentences into syntactically meaningful parts, which aids in information extraction for tasks such as search algorithms, recommendation systems, and customer feedback analysis.</p> Signup and view all the answers

<p>Statistical Models like Hidden Markov Model (HMM) capture dependencies between words HMM consists of multiplying all Transition and Emission Probability Emission: Probability of Tag (Noun) being given word (Mary) Transition: Probability of transition from one tag to another NN : Models like Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM) and Transformer Models capture complex semantic nuances Named Entity Recognition (NER) : Identification and classification of named entities Types : Person, Organization, Place, Date and Time Input: Tokens with respective POS tag Output: Tokens with POS tag + named entities are grouped and given a type Used in info extraction: Search algorithm, recommendation system, customer feedback. Chunking : Dividing sentences int.</p> Signup and view all the answers

<p>words</p> Signup and view all the answers

<p>tag</p> Signup and view all the answers

<p>entities</p> Signup and view all the answers

<p>Time</p> Signup and view all the answers

<p>int</p> Signup and view all the answers