Bigrams: Analytics Vidhya, Vidhya is, is a, a great, great source, source to, To learn, learn data, data science
When dealing with text data, NLP can be utilized for feature extraction, comparing feature similarity, and creating text vector features.
To minimize the dimensionality of the data, any technique can be utilized.
None of these expressions would be able to recognize the dates in this text item.
The Lesk method, which is the only one that may be employed, is option 2, and it is used to decipher word senses.
Levenshtein is used to calculate the distance between dictionary terms, and collaborative filtering can be used to examine human usage trends.
Stopwords are removed, and punctuation is replaced, resulting in the text "Analytics vidhya fantastic source study data science." Trigrams: amazing source learn, source learn data, source learn data science, analytics vidhya,
The most important terms in a corpus can be extracted using any of the strategies.
The key distinction is that CRF models the conditional probability of the output given the input directly, making it a discriminative model, while HMM models the joint probability of the input and output, making it a generative model. The choice between CRF and HMM depends on the nature of the task and the type of data being modeled.
These relationships are extracted from the text through dependency and constituent parsing.
There is no goal variable because you are simply provided the data from the tweets and nothing else. Since svm and naive bayes are both supervised learning methods, one cannot train a supervised learning model.
The correct answers are 1 and 2 because removing stopwords will reduce the amount of features in the matrix, normalizing words will lessen redundant features, and changing all words to lowercase would minimize dimensionality.
Both retrieval-based and generative models have their strengths and weaknesses. Retrieval-based models can provide accurate and controlled responses since they rely on predefined rules, but they may be limited in handling unseen or out-of-context queries. Generative models, on the other hand, can produce more diverse and contextually appropriate responses, but they require more data and may sometimes generate incorrect or irrelevant responses. Each approach is suitable for different chatbot applications based on the desired level of flexibility and control.
The correct interpretation of the alpha and beta hyperparameters in the Latent Dirichlet Allocation (LDA) model for text classification purposes is: Alpha (α): This hyperparameter represents the density of topics generated within documents. It controls the mixture of topics within individual documents. A higher value of alpha encourages documents to contain a more diverse mixture of topics, while a lower value makes documents focus on fewer dominant topics. Beta (β): This hyperparameter represents the density of terms generated within topics. It controls the mixture of words within each topic. A higher value of beta encourages topics to contain a more diverse set of words, while a lower value makes topics more focused on a few dominant words. So, the correct statement is. Alpha: density of topics generated within documents, beta: density of terms generated within topics - True
