Nitin Solanki

What are the different types of traditional information extraction?

There are multiple approaches to automatically perform the extraction, 1. Rule-based Approach: In this approach we already predefined rules for the syntax and other grammatical characteristics of the language which plays an important part in extracting info from text. 2. Supervised: Consider a sentence A. It has 2…

Data Science

Answered Nov 9, 2019 · 1.7K Views Read answer →

A user is trying to find out only the ORG tags using Spacy from a text body. How to do that?

For extracting specific entities we can import Spacy and pass doc.ents to the functionBut this will print all the tags present in the text. Here, X.label_ holds the name of the entity, so all we need is add a condition to only return those tuples where X.label_ equals ORG:

Data Science

Answered Nov 8, 2019 · 1.5K Views Read answer →

Explain 11pt precision

This is a measure of precision at recall levels of 0%, 10%, 20%, . . . , 100%. For each level, the fraction of relevant documents is determined by going down the ranked list of results until the recall level is reached. Interpolation of point is done to reach the particular recall levels.

Data Science

Answered Nov 7, 2019 · 1.6K Views Read answer →

What are different approaches to information extraction?

IE divides into two subclasses which are, Traditional information extraction, predefined relationships between entities. Open information extraction, relationships are not predefined between entities. The system has complete freedom to extract any sort of relationship, it comes across while going through the text data.

Data Science

Answered Nov 7, 2019 · 1.6K Views Read answer →

How to implement normalization in Python?

Normalization scales the numeric feature variables in the range of 0 to 1. To implement normalization using Python, we can use the iris dataset.

Data Science

Answered Nov 7, 2019 · 1.7K Views Read answer →

What are the missing value imputation techniques?

The relaxed and quickest method to a missing data problem is dropping the offending entries. But we have to take care that dropping data not at random is dangerous and dropping too much data is also dangerous. Dropping features with great nullity. A feature that has a high no of empty values is suspected to be very…

Data Science

Answered Nov 5, 2019 · 1.5K Views Read answer →

How to plot a linear regression to a double logarithmic R plot?

Suppose we have following data someFactor = 500 x = c(1:250) y = x^-.25 * someFactor Easiest way to add a regression line to plot lines(log(x), exp(predict(model, newdata=list(x=log(x)))) ,col="red") The range of values for x plotted on the log-scale and for log(x) being used as the independent variable are actually…

Data Science

Answered Nov 5, 2019 · 1.5K Views Read answer →

How to fit a multiple linear regression model on 1664 explanatory variables in R?

So we need to choose from the 1664 variables a legitimate model, for example a model that predicts as a significant part of the inconsistency in the information with as scarcely any logical variables. There are a few different ways of doing this: Utilizing master information to choose variables that are known to be…

Data Science

Answered Nov 5, 2019 · 1.4K Views Read answer →

Name a possible method of improving the accuracy of a linear regression model?

We do this in many ways but one of the most preferred methods of improving the accuracy of linear regression models is ‘The outlier treatment’. Outliers have excessive significance in linear regression since regression is very penetrating to outliers. Thus, it becomes crucial to treat outliers with suitable values. It…

Data Science

Answered Nov 5, 2019 · 1.5K Views Read answer →

A user wants to learn about dplyr and wants to group by multiple columns by using input as a string vector.Below is his code.How to fix that?

# make data with weird column names that can't be hard coded data = data.frame( asihckhdoydkhxiydfgfTgdsx = sample(LETTERS[1:3], 100, replace=TRUE), a30mvxigxkghc5cdsvxvyv0ja = sample(LETTERS[1:3], 100, replace=TRUE), value = rnorm(100) ) # get those columns which we want the average columns = names(data)[-3] # plyr -…

Data Science

Answered Nov 5, 2019 · 1.5K Views Read answer →

A user wants to pass in the value inside the lapply function but it fails to retain the value.How to fix that?

lapply(names(RFEresults), function(x) { feats

Data Science

Answered Nov 5, 2019 · 1.5K Views Read answer →

What is a function and how to create a function using R?

Formally, a function is a useful device that groups together a set of statements so they can be run more than once. They can also let us specify parameters that can serve as inputs to the functions. On a more fundamental level, functions allow us to not have to repeatedly write the same code again and again. Let us…

Data Science

Answered Nov 5, 2019 · 1.7K Views Read answer →