Named Entity Recognition is an information extraction task that serves as a
preprocessing step for other natural language processing tasks, such as machine
translation, information retrieval, and question answering. Named entity
recognition enables the identification of proper names as well as temporal and
numeric expressions in an open domain text. For Semitic languages such as
Arabic, Amharic, and Hebrew, the named entity recognition task is more
challenging due to the heavily inflected structure of these languages. In this
paper, we present an Amharic named entity recognition system based on
bidirectional long short-term memory with a conditional random fields layer. We
annotate a new Amharic named entity recognition dataset (8,070 sentences, which
has 182,691 tokens) and apply Synthetic Minority Over-sampling Technique to our
dataset to mitigate the imbalanced classification problem. Our named entity
recognition system achieves an F_1 score of 93%, which is the new
state-of-the-art result for Amharic named entity recognition.

本研究基于双向长短时记忆和条件随机场层构建阿姆哈拉语命名实体识别系统，并通过合成少数类过采样技术减轻分类问题，最终获得 93％的 F_1 得分，成为阿姆哈拉语命名实体识别的新的最优结果。

ANEC：一个阿姆哈拉语命名实体语料库及基于 Transformer 的识别器

ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer

Semitic languages can be highly ambiguous, having several interpretations of
the same surface forms, and morphologically rich, having many morphemes that
realize several morphological features. This is further exacerbated for
dialectal content, which is more prone to noise and lacks a standard
orthography. The morphological features can be lexicalized, like lemmas and
diacritized forms, or non-lexicalized, like gender, number, and part-of-speech
tags, among others. Joint modeling of the lexicalized and non-lexicalized
features can identify more intricate morphological patterns, which provide
better context modeling, and further disambiguate ambiguous lexical choices.
However, the different modeling granularity can make joint modeling more
difficult. Our approach models the different features jointly, whether
lexicalized (on the character-level), where we also model surface form
normalization, or non-lexicalized (on the word-level). We use Arabic as a test
case, and achieve state-of-the-art results for Modern Standard Arabic, with 20%
relative error reduction, and Egyptian Arabic (a dialectal variant of Arabic),
with 11% reduction.

该研究使用阿拉伯语探讨了联合建模的方法，具体包括字符级别的词汇化特征和字词级别的非词汇化特征，可以更好地将模糊的语言特征解模糊，进一步提高模型的效果和词义的准确性。该方法在现代标准阿拉伯语和阿拉伯埃及话方言的翻译中都取得了相对较好的实验结果。