Tag: NLP

Suppose you build word vectors (embeddings) with each word vector having dimensions as the vocabulary size(V) and feature values as pPMI between corresponding words: What are the problems with this approach and how can you resolve them ?

Posted on by InterviewBuddy

Please read here to understand what is PMI and pPMI. Problems As the vocabulary size (V) is large, these vectors will be large in size. They will be sparse as a word may not have co-occurred with all possible words. Resolution Dimensionality Reduction using approaches like Singular Value Decomposition (SVD) of the term document matrix…

What is speaker segmentation in speech recognition ? How do you use it ?

Posted on by InterviewBuddy

Speaker diarization or speaker segmentation is the process of automatically assigning a speaker identity to each segment of the audio file. Segmenting by speaker is very useful in several applications  to understand who said what in a conversation. Typically speaker information is crucial for applications such as emotion detection, behavioural analysis or topic analysis of…

What are some common tools available for NER ? Named Entity Recognition ?

Posted on by InterviewBuddy

Notable NER platforms include: GATE supports NER across many languages and domains out of the box, usable via a graphical interface and a Java API. OpenNLP includes rule-based and statistical named-entity recognition. SpaCy features fast statistical NER as well as an open-source named-entity visualizer.  

What is the difference between word2Vec and Glove ?

Posted on by InterviewBuddy

Word2Vec is a Feed forward neural network based model to find word embeddings. The Skip-gram model, modelled as predicting the context given a specific word, takes the input as each word in the corpus, sends them to a hidden layer (embedding layer) and from there it predicts the context words. Once trained, the embedding for a particular…

What is the difference between paraphrasing and textual entailment ?

Posted on by InterviewBuddy

Textual entailment is the process of determining if a source T implies the hypothesis text H. Example :It is a unidirectional relationship : text: If you help the needy, God will reward you. hypothesis: Giving money to a poor man has good consequences. Some techniques for textual entailment include lexical similarity based techniques to identify…

What are the state of the art techniques for Machine Translation ?

Posted on by InterviewBuddy

Machine translation can be done by either of the following techniques : Rule based machine translation (Older techniques) : Uses dictionary between words of the two languages along with syntactic, semantic morphological analysis of the source sentence to define context. Linguistic Rules are defined to translate a specific word in a given context into target…

How do you design a system that reads a natural language question and retrieves the closest FAQ answer?

Posted on by InterviewBuddy

There are multiple approaches for FAQ based question answering Keyword based search (Information retrieval approach): Tag each question with keywords. Extract keywords from query and retrieve all relevant questions answers. Easy to scale with appropriate indexes reverse indexing. Lexical matching approach : word level overlap between query and question. These approaches might be harder to…

How do you deal with dataset imbalance in a problem like spam filtering ?

Posted on by InterviewBuddy

Class imbalance is a very common problem when applying ML algorithms. Spam filtering is one such application where class imbalance is apparent. There are many more non-spam emails in a typical inbox than spam emails. The following approaches can be used to address the class imbalance problem. Designing an Asymmetric cost function where the cost…

You have come up with a Spam classifier. How do you measure accuracy ?

Posted on by InterviewBuddy

Spam filtering is a classification problem. In a classification problem, the following are the common metrics used to measure efficacy : True positives : Those data points where the outcome is spam and the document is actually spam. True Negatives: Those data points where the outcome is not spam and the document is actually not…