Recall-Oriented Learning of Named Entities in Arabic Wikipedia

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 162–173, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Recall-Oriented Learning of Named Entities in Arabic Wikipedia Behrang Mohit∗ Nathan Schneider† Rishav Bhowmick∗ Kemal Oflazer∗ Noah A. Smith† School of Computer Science, Carnegie Mellon University ∗P.O. Box 24866, Doha, Qatar †Pittsburgh, PA 15213, USA {behrang@,nschneid@cs.,rishavb@qatar.,ko@cs.,nasmith@cs.}cmu.edu Abstract We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adap- tation setting for which we have no labeled training data in the target domain. To fa- cilitate evaluation, we obtain annotations for articles in four topical groups, allow- ing annotators to identify domain-specific entity types in addition to standard cate- gories. Standard supervised learning on newswire text leads to poor target-domain recall. We train a sequence model and show that a simple modification to the online learner—a loss function encouraging it to “arrogantly” favor recall over precision— substantially improves recall and F1. We then adapt our model with self-training on unlabeled target-domain data; enforc- ing the same recall-oriented bias in the self- training stage yields marginal gains.1 1 Introduction This paper considers named entity recognition (NER) in text that is different from most past re- search on NER. Specifically, we consider Arabic Wikipedia articles with diverse topics beyond the commonly-used news domain. These data chal- lenge past approaches in two ways: First, Arabic is a morphologically rich lan- guage (Habash, 2010). Named entities are ref- erenced using complex syntactic constructions (cf. English NEs, which are primarily sequences of proper nouns). The Arabic script suppresses most vowels, increasing lexical ambiguity, and lacks capitalization, a key clue for English NER. Second, much research has focused on the use of news text for system building and evaluation. Wikipedia articles are not news, belonging instead to a wide range of domains that are not clearly 1The annotated dataset and a supplementary document with additional details of this work can be found at: http://www.ark.cs.cmu.edu/AQMAR delineated. One hallmark of this divergence be- tween Wikipedia and the news domain is a dif- ference in the distributions of named entities. In- deed, the classic named entity types (person, or- ganization, location) may not be the most apt for articles in other domains (e.g., scientific or social topics). On the other hand, Wikipedia is a large dataset, inviting semisupervised approaches. In this paper, we describe advances on the prob- lem of NER in Arabic Wikipedia. The techniques are general and make use of well-understood building blocks. Our contributions are: • A small corpus of articles annotated in a new scheme that provides more freedom for annota- tors to adapt NE analysis to new domains; • An “arrogant” learning approach designed to boost recall in supervised training as well as self-training; and • An empirical evaluation of this technique as ap- plied to a well-established discriminative NER model and feature set. Experiments show consistent gains on the chal- lenging problem of identifying named entities in Arabic Wikipedia text. 2 Arabic Wikipedia …

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