B44.1 WT Content Management, Such- und Texttechnologien (SL) - 3. Zug- WiSe2020/21

Credits

Original owner of project/code

• Prof. Dr. Hendrik Gärtner

Student Contributers:

• Björn Uhlig
• Lennart Döring

Intro

Combine two essentially connected fields:

• (A) duplicate recognition using entity resolution
• (B) data reduction using min hashing algorithm

Avoid Not Serializable Errors

• function params cannot be passed directly:

import Utils.tokenize
def myFunc(s:String,param:Any) = { Utils.tokenize(s,param) } // does not work

def myFunc(s:String,param:Any) = { val mp = param; Utils.tokenize(s,mp) } // does work

=> not serializable

Task A: Create basic functions for text analysis (entity resolution)

See Chapter 3 (pp. 73-103) of Mining Massive Datasets for min hashing and local sensitivity hashing.

Entity Resolution ( Text Analysis )

Implement functions in: src/main/scala/textanalyse/EntityResolution.scala Implement TF-IDF:

TFt,d is the number of occurrences of t in document d.
DFt is the number of documents containing the term t.
N is the total number of documents in the corpus.

    Wt,d = TFt,d log (N/DFt)

• tokenize
• getTokens
• countTokens
• findBiggestRecord
• calculateTF_IDF
• computeSimilarity
• calculateDotProduct
• calculateNorm
• calculateCosinusSimilarity
• calculateDocumentSimilarity
• computeSimilarityWithBroadcast
• getTermFrequencies
• createCorpus
• calculateIDF
• simpleSimilarityCalculation
• findSimilarity
• simpleSimilarityCalculationWithBroadcast
• evaluateModel

Task B

Min Hashing

Implement functions in: src/main/scala/minhash/JaccardSimilarity.scala

• calculateJaccardDistanceSet
• calculateJaccardDistanceBag
• createHashFuntions
• minHash

Local Sensitivity Hashing

Scalable Entity Resolution ( Text Analysis )

Implement functions in: src/main/scala/textanalyse/ScalableEntityResolution.scala

• buildInverseIndex
• determineCommonTokens
• calculateSimilaritiesFullDataset
• fastCosinusSimilarity