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| − | == Citation ==
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| − | Einat Minkov, Richard C. Wang & William W. Cohen, Extracting Personal Names from Emails:
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| − | Applying Named Entity Recognition to Informal Text, in HLT/EMNLP 2005
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| − | == Online version ==
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| − | [http://www.cs.cmu.edu/~einat/email.pdf Extracting Personal Names from Emails]
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| − | == Summary ==
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| − | Task: extract person names from emails
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| − | Techniques: treating NER as tagging. CRF model is used for this task.
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| − | Contribution:
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| − | * email-specific feature set.
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| − | * The authors found that repetitions within single document are more often in newwires while repetitions occurred in multiple files are more often in emails.
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| − | Based on this discovery, the authors introduced a new recall-enhancing method which is appropriate for emails.
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| − | Recall-enhancing Techniques:
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| − | * single document repetition (SDR): mark repeated tokens within a single document as a name.
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| − | * multiple document repetition (MDR): mark repeated tokens appearing in multiple documents as a name.
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| − | * inferred dictionaries: Build a dictionary from preliminary names from an extractor learned from training data.
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| − | Then, perform filtering process based on predicted frequency (PF) and inverse document frequency (IDF).
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| − | Words with low PF.IDF scores are either highly ambiguous in the corpus or the common words, which inaccurately predicted as names by the extractor.
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| − | * PF: measures the ratio between the number of times that a word predicted as part of a name and the number of occurrences of this word.
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| − | * IDF: measures word frequency.
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| − | == Related papers ==
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| − | * '''Search-based Structured Prediction''': This is the journal version of the paper that introduces the [[UsesMethod::SEARN]] algorithm - [[RelatedPaper::Daume_et_al,_ML_2009]].
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