Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically-Rich Languages (MRLs) pose a challenge to this basic formulation, as the boundaries of Named Entities do not  coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental modeling questions: (i) What should be the basic units to be identified and labeled, are they token-based or morpheme-based? and (ii) How can morphological units be encoded and accurately obtained in realistic (non-gold) scenarios? We empirically investigate these questions on a novel parallel NER benchmarkwe deliver , with parallel token-level and morpheme-level NER annotations  for Modern Hebrew, a morphologically complex language. Our results show that explicitly modeling morphological boundaries consistently leads to improved NER performance, and that a novel hybrid architecture that we propose , in which NER precedes and prunes the morphological decomposition(MD) space , greatly outperforms the standard pipeline approach, on both Hebrew NER and Hebrew MD in realistic scenarios .
Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically-Rich Languages (MRLs) pose a challenge to this basic formulation, as the boundaries of Named Entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings, i.e., where no gold morphology is available. We empirically investigate these questions on a novel NER benchmark , with parallel tokenlevel and morpheme-level NER annotations , which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture , in which NER precedes and prunes morphological decomposition , greatly outperforms the standard pipeline , where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks .
A
necessarily
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modeling questions: (i) What should be
questions, namely, what are
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identified and labeled, are they token-based or morpheme-based? and (ii) How can morphological units be encoded and accurately obtained in realistic (non-gold) scenarios?
labeled, and how can these units be detected and classified in realistic settings, i.e., where no gold morphology is available.
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parallel NER benchmarkwe deliver
NER benchmark
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token-level
tokenlevel
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, which we develop
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complex
rich-and-ambiguous
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consistently
D
that we propose
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the morphological decomposition(MD) space
morphological decomposition
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approach, on
, where morphological decomposition strictly precedes NER, setting a new performance bar for
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MD in realistic scenarios
morphological decomposition tasks