Reference Scope Identification in Citing Sentences
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This document describes an approach to identify the scope of references in sentences containing multiple citations. The approach uses a conditional random field (CRF) sequence model with features including distance, position, part-of-speech tags, and syntactic dependencies. It first segments sentences using punctuation and conjunctions, then applies the CRF and a majority voting scheme to label each word as inside or outside the scope. The method achieved over 90% accuracy on a dataset of citations from ACL papers.
![Approach 3-S1-* (CRF/segment)
● segmentation (1)
● punctuation marks
● coordination conjunctions
– and, but, for, nor, or, so, yet
● a set of special expressions
– "for example", "for instance", "including", "includes",
"such as", "like", etc.
● [Rerankers have been successfully applied to numerous
NLP tasks such as] [parse selection (GTREF)], [parse
reranking (GREF)], [question-answering (REF)].](https://image.slidesharecdn.com/kameda-citation-121011035139-phpapp01/85/Reference-Scope-Identification-in-Citing-Sentences-7-320.jpg)
![Approach 3-S2-* (CRF/segment)
● segmentation (2)
● chunking tool
– noun groups
– verb groups
– preposition groups
– adjective groups
– adverb groups
– other parts form segment by themselves
● [To] [score] [the output] [of] [the coreference models], [we]
[employ] [the commonly-used MUC scoring program (REF)]
[and] [the recently-developed CEAF scoring program (TREF)].](https://image.slidesharecdn.com/kameda-citation-121011035139-phpapp01/85/Reference-Scope-Identification-in-Citing-Sentences-8-320.jpg)
![Approach 3-*-R1,2,3
(CRF/segment)
● R1: majority label of the words it contains
● R2: inside if any word is inside
● R3: outside if any word is outside
● [I O O O O] [I I I] [O O]](https://image.slidesharecdn.com/kameda-citation-121011035139-phpapp01/85/Reference-Scope-Identification-in-Citing-Sentences-9-320.jpg)
![[Paper review] BERT](https://cdn.slidesharecdn.com/ss_thumbnails/paperreviewbert-190507052754-thumbnail.jpg?width=560&fit=bounds)
![Fafl notes [2010] (sjbit)](https://cdn.slidesharecdn.com/ss_thumbnails/faflnotes2010sjbit-131208034715-phpapp02-thumbnail.jpg?width=560&fit=bounds)
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Reference Scope Identification in Citing Sentences
- 1. Reference Scope Identification in Citing Sentences Authors: Amjad Abu-Jbara, Dragomir Radev (University of Michigan) Conference: NAACL 2012 Expositor: Akihiro Kameda (Aizawa Lab. The University of Tokyo)
- 2. Abstract ● Problem: ● Multiple citation in one sentence ● There are many POS taggers developed using different techniques for many major languages such as transformation-based error-driven learning (Brill, 1995), decision trees (Black et al., 1992), Markov model (Cutting et al., 1992), maximum entropy methods (Ratnaparkhi, 1996) etc for English. ● Approach:Prepossessing and 2+1+2*3+1=10 methods
- 4. Reference Preprocessing (tagging, grouping, non-syntactical element removal) ● These constraints can be lexicalized (REF.1; REF.2), unlexicalized (REF.3; TREF.4) or automatically learned (REF.5; REF.6). ● These constraints can be lexicalized (GREF.1), unlexicalized (GTREF.2) or automatically learned (GREF.3). ● (GTREF.1) apply fuzzy techniques for integrating source syntax into hierarchical phrase-based systems (REF.2).
- 5. Approach 1(SVM,LR) ● Word classification ● with SVM, a logistic regression classifier ● Feature: Distance, Position(Before/After), in Segment(,.; and, but, for, nor, or, so, yet), POS tag, Dependency Distance, Dependency Relations, Common Ancestor Node, Syntactic Distance ● Problem Example: ● There are many POS taggers developed using different techniques for many major languages such as transformation- based error-driven learning (Brill, 1995), decision trees (Black et al., 1992), Markov model (Cutting et al., 1992), maximum entropy methods (Ratnaparkhi, 1996) etc for English.
- 6. Approach 2(CRF) ● Sequence Labeling with CRF ● feature is same as Approach 1
- 7. Approach 3-S1-* (CRF/segment) ● segmentation (1) ● punctuation marks ● coordination conjunctions – and, but, for, nor, or, so, yet ● a set of special expressions – "for example", "for instance", "including", "includes", "such as", "like", etc. ● [Rerankers have been successfully applied to numerous NLP tasks such as] [parse selection (GTREF)], [parse reranking (GREF)], [question-answering (REF)].
- 8. Approach 3-S2-* (CRF/segment) ● segmentation (2) ● chunking tool – noun groups – verb groups – preposition groups – adjective groups – adverb groups – other parts form segment by themselves ● [To] [score] [the output] [of] [the coreference models], [we] [employ] [the commonly-used MUC scoring program (REF)] [and] [the recently-developed CEAF scoring program (TREF)].
- 9. Approach 3-*-R1,2,3 (CRF/segment) ● R1: majority label of the words it contains ● R2: inside if any word is inside ● R3: outside if any word is outside ● [I O O O O] [I I I] [O O]
- 10. AR2011 the link grammar parser (Sleator and Temperley,1991)
- 11. Experiment
- 12. Data ● ACL Anthology Network Corpus ● 3300 sentences, citations in each ≧ 2 Annotation agreement ● 500 of 3300, ● Preprocessing is perfect ● Kappa coefficient of scope is P ( A)−P ( E ) K= =2P ( A)−1=0.61 1−P ( E )
- 13. Tools ● Edinburgh Language Technology Text Tokenization Toolkit (LT-TTT) ● text tokenization, part-of-speech tagging, chunking, and noun phrase head identification. ● Stanford parser ● syntactic and dependency parsing ● LibSVM with linear kernel ● Weka ● logistic regression classification
- 14. Tools ● Machine Learning for Language Toolkit (MALLET) ● CRF Validation ● 10-fold cross validation
- 15. Experiment (Preprocessing) These constraints can be lexicalized (REF.1; REF.2), ll r ec a ● unlexicalized (REF.3; TREF.4) or and 93 .1%learned (REF.5; REF.6). 3% preci s ion automatically ng: 9 8 . Taggi ● These constraints can be lexicalized (GREF.1), unlexicalized (GTREF.2) or Perfect! automatically learned (GREF.3). Grouping: (GTREF.1) apply fuzzy techniques for integrating source a l: ● syntax into hierarchicalence removsystems (REF.2). Non-syn tactic refer phrase-based ecall 9 0. 1% r cision and 9 0.08% pre
- 16. Experiment (Main) ● CRF ● Chunking ● Majority
- 17. Feature Analysis ● Feature: Distance, Position(Before/After), Same segment(,.; and, but, for, nor, or, so, yet), POS tag, Dependency Distance, Dependency Relations, Common Ancestor Node, Syntactic Distance
- 18. Summary ● Identified reference scope in a sentence which has multiple citation ● CRF ● Chunking ● Majority