Citation Provenance FYP/Research Update WING Meeting 28 Sept 2012 Heng Low Wee 1/5/2016 1.

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Citation Provenance FYP/Research Update WING Meeting 28 Sept 2012 Heng Low Wee 1/5/2016 1

Previous Update  Motivation  Reading experience; Interrupts reading when looking up cited paper.  Goal: Predict type of citation; Location of cited information  Problem Analysis  General/Specific citations  Citing context as query, fragments of cited paper as ‘documents’ to be matched 1/5/2016 2

Previous Update (Continued)  Corpus  ACL Anthology Reference Corpus, processed with ParsCit to extract citing contexts, and fragments of cited paper  Approach  1 st feature considered: Cosine Similarity  Annotations 1/5/2016 3

Outline  Previous Update  Features Added  Annotating Data  Initial Testing  Analysis  What’s Next? 1/5/2016 4

Features Added  Citation Density  The no. of inline citations / no. of lines in the context  Intuition: High density hints it is a general citation  (Dong & Schafer, 2011) [ACL I ]  Difference in Publishing Year  Intuition: Large difference suggests citing older and fundamental work; less discussion on citing paper thus general citation 1/5/2016 5

Features Added  Location of Inline Citation  The section in which the inline citation belongs  Intuition: If located in Introduction, suggests general citation  (Dong & Schafer, 2011) [ACL I ]  Title Overlap & Author Overlap  Jaccard distance between citing’s and cited’s  Intuition: Similar titles suggests closely related work, refers to cited for specific contributions; Same authors hints closely related work 1/5/2016 6

Features Added  Average TF-IDF weight for contexts and fragments in cited paper  Intuition: Specific citations refer to ‘high valued’ terms in cited paper  Cosine Similarity 1/5/2016 7

Annotating Data  Previous scheme  Annotate plain text file using labels + line number range  Annotating by line range  difficult to determine whether prediction matches annotation because they are not discrete  Annotation task is very challenging  4 annotation labels  General (0), Specific-Yes (1), Specific-No (2), Undetermined (3)  For each citing context in citing paper, for each text block in cited paper: annotate with label 1/5/2016 8

Annotating Data 1/5/ Citing Cited : : L1L1 L2L2 LjLj LnLn : :

Annotating Data  Currently:  6632 annotated records  ~62% General, ~3% Specific-Yes, ~34% Specific-No, ~0.6% Undetermined  Undetermined data points are removed; Specific-No data points are regarded as General  Reduced to binary classification 1/5/

Initial Testing  90% train; 10% test; SVC; 1 iteration 1/5/ – General, 1 – Specific-Yes

Analysis  Unable to predict any ‘Specific-Yes’  Number of ‘Yes’ instances too little.  Feature set unable to distinguish General vs Specific 1/5/

What’s Next  To investigate further: where and how specific citations are made  Features that can better distinguish general vs specific citations 1/5/