The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.
The traditional annotation projection approaches @cite_25 @cite_24 @cite_3 project NER tags across language pairs using parallel corpora or translations. proposed a variant of annotation projection which projects expectations of tags and uses them as constraints to train a model based on generalized expectation criteria. Annotation projection has also been applied to several other cross-lingual NLP tasks, including word sense disambiguation @cite_10 , part-of-speech (POS) tagging @cite_25 and dependency parsing @cite_14 .
@cite_14 @cite_3 @cite_24 @cite_10 @cite_25
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As developers of a highly multilingual named entity recognition (NER) system, we face an evaluation resource bottleneck problem: we need evaluation data in many languages, the annotation should not be too time-consuming, and the evaluation results across languages should be comparable. We solve the problem by automatically annotating the English version of a multi-parallel corpus and by projecting the annotations into all the other language versions. For the translation of English entities, we use a phrase-based statistical machine translation system as well as a lookup of known names from a multilingual name database. For the projection, we incrementally apply different methods: perfect string matching, perfect consonant signature matching and edit distance similarity. The resulting annotated parallel corpus will be made available for reuse. While significant effort has been put into annotating linguistic resources for several languages, there are still many left that have only small amounts of such resources. This paper investigates a method of propagating information (specifically mention detection information) into such low resource languages from richer ones. Experiments run on three language pairs (Arabic-English, Chinese-English, and Spanish-English) show that one can achieve relatively decent performance by propagating information from a language with richer resources such as English into a foreign language alone (no resources or models in the foreign language). Furthermore, while examining the performance using various degrees of linguistic information in a statistical framework, results show that propagated features from English help improve the source-language system performance even when used in conjunction with all feature types built from the source language. The experiments also show that using propagated features in conjunction with lexically-derived features only (as can be obtained directly from a mention annotated corpus) yields similar performance to using feature types derived from many linguistic resources. We present an unsupervised method for word sense disambiguation that exploits translation correspondences in parallel corpora. The technique takes advantage of the fact that cross-language lexicalizations of the same concept tend to be consistent, preserving some core element of its semantics, and yet also variable, reflecting differing translator preferences and the influence of context. Working with parallel corpora introduces an extra complication for evaluation, since it is difficult to find a corpus that is both sense tagged and parallel with another language; therefore we use pseudo-translations, created by machine translation systems, in order to make possible the evaluation of the approach against a standard test set. The results demonstrate that word-level translation correspondences are a valuable source of information for sense disambiguation. This paper describes a system and set of algorithms for automatically inducing stand-alone monolingual part-of-speech taggers, base noun-phrase bracketers, named-entity taggers and morphological analyzers for an arbitrary foreign language. Case studies include French, Chinese, Czech and Spanish.Existing text analysis tools for English are applied to bilingual text corpora and their output projected onto the second language via statistically derived word alignments. Simple direct annotation projection is quite noisy, however, even with optimal alignments. Thus this paper presents noise-robust tagger, bracketer and lemmatizer training procedures capable of accurate system bootstrapping from noisy and incomplete initial projections.Performance of the induced stand-alone part-of-speech tagger applied to French achieves 96 core part-of-speech (POS) tag accuracy, and the corresponding induced noun-phrase bracketer exceeds 91 F-measure. The induced morphological analyzer achieves over 99 lemmatization accuracy on the complete French verbal system.This achievement is particularly noteworthy in that it required absolutely no hand-annotated training data in the given language, and virtually no language-specific knowledge or resources beyond raw text. Performance also significantly exceeds that obtained by direct annotation projection.