Author- Paper Identification Problem

Guided ByProf Duc Tran

Team :

Karthik Reddy Vakati

Nachammai C

Pooja Mishra

Problem Statement

To determine the correct author from the author’s dataset for a particular paper.

Ambiguity in author names might cause a paper to be assigned to the wrong author, which leads to noisy author profiles

Challenge is to determine which papers in an author profile were truly written by a given author

Data Points

The data points include all papers written by an author, his affiliation (University, Technical Society, Groups).

Paper-Author -( PaperId , AuthorId, Name, Affiliation)

The meta data includes journals written by him and conferences attended by an author.

Paper -( Id, Title, Year, ConferenceId , JournalId, Keywords)

Author -( Id, Name, Affiliation) Conference-(Id, ShortName,FullName,HomePage) Journal -(Id, ShortName, FullName, HomePage)

Machine Learning Task

• Feature Engineering

• Algorithms

• Model Tuning

• Results

• Evaluation

Steps Taken to Solve Problem

Data preprocessing and cleaning Feature engineering Choose a model - Random Forest/Gradient Boost Model Building the model Evaluating the results Extracting feature values Building the model using the modified train file Evaluating the results Tuning the model

Data preprocessing and cleaning

Issues with data The csv files needed cleaning Few had attributes spilled over 3 rows Some rows had more attributes than the required

number of attributes Special characters caused issue

Wrote a Perl script to Clean data and format it

Issues with data-I

Issues with data-II

Feature Engineering Steps

• Aggregation: combining multiple features into one.

How did we use: Elaborated train file with AuthorID, PaperID and Confirmation combined with name and affiliation from Author file.

• Discretization: Converting continuous features or variables to discretized or nominal features

How did we use: The year the paper is published. The max and min years the author was actively publishing papers.

• Construction: Creating new features out of original ones

How did we use: Keywords in Paper

Author features

• distance between the author names in paper-author and author files

• matched substring ratio between the author names in paper-author and author files

• keywords used by a particular author(less weight)

• count keywords for author

• count the no of co-authors for a given co-author

• weighted TF-IDF measure of all author keywords inside author's papers

• count different papers of author

• years during which the author wrote many papers

• number of times an author is repeated (sum for distinct ids)

• list of distinct ids assigned to the same author

Paper features

• year of paper

• count authors of paper

• count duplicated papers for paper

• count duplicated authors for paper

• count keywords in paper

• how many time the exact same set of authors is repeated in different papers

(without

duplicates)

Paper-Author features

• correct affiliation from the table PaperAuthor: binary feature

• which year the author publishes (for the first year papers of author this feature equals 1 for the second year papers - 2 and so on)

• count sources: number of times pair author-paper is appeared in the table PaperAuthor

• are names in PaperAuthor table and Author table the same

Machine Learning Models Used

Models used earlier

• K-means clustering

• ZeroR

• Tree J-48

• Naïve Bayes

Machine Learning Models Used

• RandomForest

• Gradient Boost

Feature values extraction

Count of paper for each author Maximum active year for given author Maximum active year for given author Jaccard distance between author name in author file and

paper author file Jaccard distance between affiliation in author file and paper

author file The year paper was published

Build Random forest using weka

With the elaborated train file

Build Random forest using Mahout

Build Gradient Boost using H20

Evaluation Metrics

• Accuracy

• Error percentage

Lessons Learned!

When you know what to find out exactly in the provided data ,use supervised learning model as classification rather than choosing unsupervised learning model such as clustering.

When you want to find patterns or structures in the provided data use unsupervised dlearning models such as clustering.

Try building model using the provided train file but it might not give you better results always. You can try to modify it using the existing data but making sure you do no change it.

Choosing the features is the most important thing and we can extract the feature values from the given data and use it to build the model.

Choosing features from different data points will give better results than just choosing them from only one.

Thank you!!