# AI News, Using Machine Learning to Measure Job Skill Similarities

- On Wednesday, October 17, 2018
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## Using Machine Learning to Measure Job Skill Similarities

This project involved implementing machine learning methodologies to identify similarities in job skills contained in resumes.

The result is a vector space that embeds meaning into its dimensions, such that a) words close to each other in the vector space are more likely to share meanings, and b) each dimension represents meaning in a particular context.

A frequently cited example is that within a word2vec vector space, if you start with the vector representing the word king, subtract the vector representing “man”, and add the vector representing “woman”, the resulting vector will be near the vector for “queen”.

Here, we used the list of 3k skill words as the observations to cluster, based on the words' vectors in the word2vec vector space (words in both the corpus and the skill list were stemmed using the Snowball stemming methodology).

We did see some clusters that contained words that could be further divided into subclusters with different meanings, but given our arbitrarily chosen value of k, that is not surprising (and it suggests that in fact choosing a higher value of k would have broken these subclusters out into their own group).

Software Development and Data Science 2 Accounting / Project Management 3 Telecom 4 General Tech 5 Legal / Occupations / Misc 6 Big Data / Data Engineering 7 Medicine 8 Human Resources 9 General Business 10 Design and Project Management 11 Banking and Finance 12 Web Development 13 Educational Class Topics 14 Social Media 15 Sports / Arts / Travel / Media We also created the ability to review the distances between any word in the skill list and the 50 words closest to that word in the vector space.

As in k-means, hierarchical clustering groups a set of observations based on some “distance”, but instead of fixing the number of groups from the outset, the procedure is to start with each observation as its own cluster and then successively combine these clusters based on some aggregate measure of the distance between them.

For the present task, clustering word vectors based on job skills, we preferred the method of complete linkage, which considers the inter-cluster distance to be the greatest distance between any two individual observations in the clusters to be merged.

While it is difficult to learn anything from the above image (even with labels), cutting at some reasonable value of height, say 20, will give a dendrogram with just 22 clusters, a number close to the 15 groups of skills used in the previous method.

This sub-dendrogram is now somewhat more useful than the social media cluster found before because the sub grouping can easily be read off the diagram, such as how instagram and pinterest are treated as more similar, perhaps because they are more image oriented than the other platforms.

For example, we can click on the term “data”, then the circles would change their size to show the relationship between “data” and the topics. The app shows that “data” is related to every topic, and it’s more important in topic 8, 9 and 11.

Because each of them was effective in assessing overall relatedness, to a large degree the answer depends on the application in which they’re used. For example, k-means is of linear complexity while hierarchical clustering runs in quadratic time, so the size of the data to be analyzed may become very important. Also, LDA was generally designed for comparisons of documents, versus the more “local” comparisons done on a word by word basis with word2vec. So LDA may be better if the application involves things like assessing which resumes or job descriptions are most similar (see also some interesting new research combining the benefits of word2vec and LDA). Other application requirements may highlight different differences between the approaches, and drive the algorithm choice.

- On Wednesday, October 17, 2018
- By Read More

## Using Machine Learning to Measure Job Skill Similarities

This project involved implementing machine learning methodologies to identify similarities in job skills contained in resumes.

We decided to use the word2vec word embedding technique to try to assess the similarity of the entries included in the list of 3,000 skills, using the resume text as the word2vec corpus.

The result is a vector space that embeds meaning into its dimensions, such that a) words close to each other in the vector space are more likely to share meanings, and b) each dimension represents meaning in a particular context.

A frequently cited example is that within a word2vec vector space, if you start with the vector representing the word king, subtract the vector representing “man”, and add the vector representing “woman”, the resulting vector will be near the vector for “queen”.

Here, we used the list of 3k skill words as the observations to cluster, based on the words' vectors in the word2vec vector space (words in both the corpus and the skill list were stemmed using the Snowball stemming methodology).

We did see some clusters that contained words that could be further divided into subclusters with different meanings, but given our arbitrarily chosen value of k, that is not surprising (and it suggests that in fact choosing a higher value of k would have broken these subclusters out into their own group).

As in k-means, hierarchical clustering groups a set of observations based on some “distance”, but instead of fixing the number of groups from the outset, the procedure is to start with each observation as its own cluster and then successively combine these clusters based on some aggregate measure of the distance between them.

For the present task, clustering word vectors based on job skills, we preferred the method of complete linkage, which considers the inter-cluster distance to be the greatest distance between any two individual observations in the clusters to be merged.

While it is difficult to learn anything from the above image (even with labels), cutting at some reasonable value of height, say 20, will give a dendrogram with just 22 clusters, a number close to the 15 groups of skills used in the previous method.

This sub-dendrogram is now somewhat more useful than the social media cluster found before because the sub grouping can easily be read off the diagram, such as how instagram and pinterest are treated as more similar, perhaps because they are more image oriented than the other platforms.

For example, we can click on the term “data”, then the circles would change their size to show the relationship between “data” and the topics. The app shows that “data” is related to every topic, and it’s more important in topic 8, 9 and 11.

By way of example, in using this application we can find some interesting relationships: Concerning the three approaches we took – word2vec with k-means clustering, word2vec with hierarchical clustering, and Latent Dirichlet Allocation – the obvious question to ask is which was “best” in measuring similarities in job skills.

Because each of them was effective in assessing overall relatedness, to a large degree the answer depends on the application in which they’re used. For example, k-means is of linear complexity while hierarchical clustering runs in quadratic time, so the size of the data to be analyzed may become very important. Also, LDA was generally designed for comparisons of documents, versus the more “local” comparisons done on a word by word basis with word2vec. So LDA may be better if the application involves things like assessing which resumes or job descriptions are most similar (see also some interesting new research combining the benefits of word2vec and LDA). Other application requirements may highlight different differences between the approaches, and drive the algorithm choice.

- On Wednesday, October 17, 2018
- By Read More

## Unsupervised Learning and Data Clustering

A task involving machine learning may not be linear, but it has a number of well known steps: One good way to come to terms with a new problem is to work through identifying and defining the problem in the best possible way and learn a model that captures meaningful information from the data.

While problems in Pattern Recognition and Machine Learning can be of various types, they can be broadly classified into three categories: Between supervised and unsupervised learning is semi-supervised learning, where the teacher gives an incomplete training signal: a training set with some (often many) of the target outputs missing.

The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine how the data is distributed in the space, known as density estimation.

Given a set of points, with a notion of distance between points, grouping the points into some number of clusters, such that The Goals of Clustering The goal of clustering is to determine the internal grouping in a set of unlabeled data.

Clustering Algorithms Clustering algorithms may be classified as listed below: In the first case data are grouped in an exclusive way, so that if a certain data point belongs to a definite cluster then it could not be included in another cluster.

The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori.

The objective function where is a chosen distance measure between a data point xi and the cluster centre cj, is an indicator of the distance of the n data points from their respective cluster centres.

A simple approach is to compare the results of multiple runs with different k classes and choose the best one according to a given criterion, but we need to be careful because increasing k results in smaller error function values by definition, but also increases the risk of overfitting.

Fuzzy k-means specifically tries to deal with the problem where points are somewhat in between centers or otherwise ambiguous by replacing distance with probability, which of course could be some function of distance, such as having probability relative to the inverse of the distance.

One should realize that k-means is a special case of fuzzy k-means when the probability function used is simply 1 if the data point is closest to a centroid and 0 otherwise.

therefore, this membership function looked like this: In the Fuzzy k-means approach, instead, the same given data point does not belong exclusively to a well defined cluster, but it can be placed in a middle way.

Hierarchical Clustering Algorithms Given a set of N items to be clustered, and an N*N distance (or similarity) matrix, the basic process of hierarchical clustering is this: Clustering as a Mixture of Gaussians There’s another way to deal with clustering problems: a model-based approach, which consists in using certain models for clusters and attempting to optimize the fit between the data and the model.

The entire data set is therefore modelled by a mixture of these distributions. A mixture model with high likelihood tends to have the following traits: Main advantages of model-based clustering: Mixture of GaussiansThe most widely used clustering method of this kind is based on learning a mixture of Gaussians: A

Expectation-Maximization tries to get around this by iteratively guessing a distribution for the unobserved data, then estimating the model parameters by maximizing something that is a lower bound on the actual likelihood function, and repeating until convergence: The Expectation-Maximization algorithm Problems associated with clustering There are a number of problems with clustering.

- On Tuesday, September 17, 2019

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**Clustering Analysis**

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