Cross-lingual Annotation Projection Research Articles

Exploiting Morpheme and Cross-lingual Knowledge to Enhance Mongolian Named Entity Recognition

Mongolian named entity recognition (NER) is not only one of the most crucial and fundamental tasks in Mongolian natural language processing, but also an important step to improve the performance of downstream tasks such as information retrieval, machine translation, and dialog system. However, traditional Mongolian NER models heavily rely on the feature engineering. Even worse, the complex morphological structure of Mongolian words makes the data sparser. To alleviate the feature engineering and data sparsity in Mongolian named entity recognition, we propose a novel NER framework with Multi-Knowledge Enhancement (MKE-NER) . Specifically, we introduce both linguistic knowledge through Mongolian morpheme representation and cross-lingual knowledge from Mongolian-Chinese parallel corpus. Furthermore, we design two methods to exploit cross-lingual knowledge sufficiently, i.e., cross-lingual representation and cross-lingual annotation projection. Experimental results demonstrate the effectiveness of our MKE-NER model, which outperforms strong baselines and achieves the best performance (94.04% F1 score) on the traditional Mongolian benchmark. Particularly, extensive experiments with different data scales highlight the superiority of our method in low-resource scenarios.

A neural approach for inducing multilingual resources and natural language processing tools for low-resource languages

AbstractThis work focuses on the rapid development of linguistic annotation tools for low-resource languages (languages that have no labeled training data). We experiment with several cross-lingual annotation projection methods using recurrent neural networks (RNN) models. The distinctive feature of our approach is that our multilingual word representation requires only a parallel corpus between source and target languages. More precisely, our approach has the following characteristics: (a) it does not use word alignment information, (b) it does not assume any knowledge about target languages (one requirement is that the two languages (source and target) are not too syntactically divergent), which makes it applicable to a wide range of low-resource languages, (c) it provides authentic multilingual taggers (one tagger forNlanguages). We investigate both uni and bidirectional RNN models and propose a method to include external information (for instance, low-level information from part-of-speech tags) in the RNN to train higher level taggers (for instance, Super Sense taggers). We demonstrate the validity and genericity of our model by using parallel corpora (obtained by manual or automatic translation). Our experiments are conducted to induce cross-lingual part-of-speech and Super Sense taggers. We also use our approach in a weakly supervised context, and it shows an excellent potential for very low-resource settings (less than 1k training utterances).

Cross-Lingual Annotation Projection for Weakly-Supervised Relation Extraction

Although researchers have conducted extensive studies on relation extraction in the last decade, statistical systems based on supervised learning are still limited, because they require large amounts of training data to achieve high performance level. In this article, we propose cross-lingual annotation projection methods that leverage parallel corpora to build a relation extraction system for a resource-poor language without significant annotation efforts. To make our method more reliable, we introduce two types of projection approaches with noise reduction strategies. We demonstrate the merit of our method using a Korean relation extraction system trained on projected examples from an English-Korean parallel corpus. Experiments show the feasibility of our approaches through comparison to other systems based on monolingual resources.

Cross-lingual annotation projection of semantic roles

This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection...