The increasing diversity of languages used on the web introduces a new level
of complexity to Information Retrieval (IR) systems. We can no longer assume
that textual content is written in one language or even the same language
family. In this paper, we demonstrate how to build massive multilingual
annotators with minimal human expertise and intervention. We describe a system
that builds Named Entity Recognition (NER) annotators for 40 major languages
using Wikipedia and Freebase. Our approach does not require NER human annotated
datasets or language specific resources like treebanks, parallel corpora, and
orthographic rules. The novelty of approach lies therein - using only language
agnostic techniques, while achieving competitive performance.
Our method learns distributed word representations (word embeddings) which
encode semantic and syntactic features of words in each language. Then, we
automatically generate datasets from Wikipedia link structure and Freebase
attributes. Finally, we apply two preprocessing stages (oversampling and exact
surface form matching) which do not require any linguistic expertise.
Our evaluation is two fold: First, we demonstrate the system performance on
human annotated datasets. Second, for languages where no gold-standard
benchmarks are available, we propose a new method, distant evaluation, based on
statistical machine translation.

本研究展示如何使用维基百科和 Freebase 自动构建 40 种主要语言的多语言命名实体识别注释器，不需要任何人工标注数据集或语言特定的资源，通过生成分布式词表示，达到竞争性的表现，并提出了一种新的远程评估方法。