David Zajic


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Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies
Benjamin Rozonoyer | David Zajic | Ilana Heintz | Michael Selvaggio
Proceedings of the Fourth International Workshop on Designing Meaning Representations

We propose the use of modal dependency parses (MDPs) aligned with syntactic dependency parse trees as an avenue for the novel task of claim extraction. MDPs provide a document-level structure that links linguistic expression of events to the conceivers responsible for those expressions. By defining the event-conceiver links as claims and using subgraph pattern matching to exploit the complementarity of these modal links and syntactic claim patterns, we outline a method for aggregating and classifying claims, with the potential for supplying a novel perspective on large natural language data sets. Abstracting away from the task of claim extraction, we prototype an interpretable information extraction (IE) paradigm over sentence- and document-level parse structures, framing inference as subgraph matching and learning as subgraph mining. We make our code open-sourced at https://github.com/BBN-E/nlp-graph-pattern-matching-and-mining.


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A Random Forest System Combination Approach for Error Detection in Digital Dictionaries
Michael Bloodgood | Peng Ye | Paul Rodrigues | David Zajic | David Doermann
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data


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Error Correction for Arabic Dictionary Lookup
C. Anton Rytting | Paul Rodrigues | Tim Buckwalter | David Zajic | Bridget Hirsch | Jeff Carnes | Nathanael Lynn | Sarah Wayland | Chris Taylor | Jason White | Charles Blake III | Evelyn Browne | Corey Miller | Tristan Purvis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We describe a new Arabic spelling correction system which is intended for use with electronic dictionary search by learners of Arabic. Unlike other spelling correction systems, this system does not depend on a corpus of attested student errors but on student- and teacher-generated ratings of confusable pairs of phonemes or letters. Separate error modules for keyboard mistypings, phonetic confusions, and dialectal confusions are combined to create a weighted finite-state transducer that calculates the likelihood that an input string could correspond to each citation form in a dictionary of Iraqi Arabic. Results are ranked by the estimated likelihood that a citation form could be misheard, mistyped, or mistranscribed for the input given by the user. To evaluate the system, we developed a noisy-channel model trained on studentsÂ’ speech errors and use it to perturb citation forms from a dictionary. We compare our system to a baseline based on Levenshtein distance and find that, when evaluated on single-error queries, our system performs 28% better than the baseline (overall MRR) and is twice as good at returning the correct dictionary form as the top-ranked result. We believe this to be the first spelling correction system designed for a spoken, colloquial dialect of Arabic.


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Using Citations to Generate surveys of Scientific Paradigms
Saif Mohammad | Bonnie Dorr | Melissa Egan | Ahmed Hassan | Pradeep Muthukrishan | Vahed Qazvinian | Dragomir Radev | David Zajic
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics


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A Methodology for Extrinsic Evaluation of Text Summarization: Does ROUGE Correlate?
Bonnie Dorr | Christof Monz | Stacy President | Richard Schwartz | David Zajic
Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization


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Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation
Bonnie Dorr | David Zajic | Richard Schwartz
Proceedings of the HLT-NAACL 03 Text Summarization Workshop