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

• Building the model using train dataset and testing

• Feature Engineering

• Algorithms

• Model Tuning

• Results

• Evaluation

Steps Taken to Solve Problem

Data preprocessing and cleaning Feature engineering Extracting feature values Creating final input file Choose a ML algorithm - Random Forest/Gradient Boost

Model Building the model Tuning the model Test the model on test data Evaluating the results

Data preprocessing and cleaning

Issues with data Few had attributes spilled over 3 rows Some rows had more attributes than the required number of

attributes Wrote a Perl script to Clean data and format it Removed stop words using NLTK package in python Converted all text to lower case Removed special characters Removed noise from years field Assigned ID to each keyword and normalized it

Issues with data-I

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 and PaperAuthor file.

• Construction: Creating new features out of original ones

How did we use: minyear and maxyear into active years

• 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.

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 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

Using Weka, Mahout and H20

• Gradient Boost

Using H20

Build Random forest using weka

With the elaborated train file

Build Random forest using H20

Feature values extraction & importance

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 Normalized Keyword ids

1. Put the data in HDFS.    $HADOOP_HOME/bin/hadoop fs -mkdir testdata $HADOOP_HOME/bin/hadoop fs -put testdata2. Build the job files.    $MAHOUT_HOME/ run: mvn clean install -DskipTests3. Generate a file descriptor for the dataset.    $HADOOP_HOME/bin/hadoop jar    $MAHOUT_HOME/core/target/mahout-core--job.jar org.apache.mahout.classifier.df.tools.Describe -p testdata/KDDTrain+.arff -f         testdata/KDDTrain+.info -d N 3 C 2 N C 4 N C 8 N 2 C 19 N L

4. Run the model    $HADOOP_HOME/bin/hadoop jar    $MAHOUT_HOME/examples/target/mahout-examples--job.jar org.apache.mahout.classifier.df.mapreduce.BuildForest -Dmapred.max.split.size=1874231 -d testdata/KDDTrain+.arff -ds testdata/KDDTrain+.info -sl 5 -p -t 100 -o nsl-forest

5. Using the Decision Forest to Classify new data.    $MAHOUT_HOME/examples/target/mahout-examples--job.jar org.apache.mahout.classifier.df.mapreduce.TestForest -i nsl-kdd/KDDTest+.arff -ds nsl-kdd/KDDTrain+.info -m nsl-forest -a -mr -o predictions

Random Forest using Mahout Steps

Evaluation Metrics

The mean average precision for N users at position n is the average of the average precision of each user, i.e.,

MAP@n=∑i=1Nap@ni/N

• Mean Average Precision

Lessons Learned!

Since we were given train and test data supervised learning is the best fit. Hence classification algorithms work better for author paper identification problem rather than clustering.

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

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.

Construct an initial set of features and try to build the model and test for its accuracy. This might not give you better results always. Construct features from features.

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

Choose initial set of weights for each feature based on its importance. This will help in model tuning.

Thank you!!