Bootstrapping a Tagged Corpus through Combination of Existing Heterogeneous Taggers

This paper describes a new method, Combi-bootstrap, to exploit existing taggers and lexical resources for the annotation of corpora with new tagsets. Combi-bootstrap uses existing resources as features for a second level machine learning module, that is trained to make the mapping to the new tagset on a very small sample of annotated corpus material. Experiments show that Combi-bootstrap: i) can integrate a wide variety of existing resources, and ii) achieves much higher accuracy (up to 44.7 % error reduction) than both the best single tagger and an ensemble tagger constructed out of the same small training sample.Comment: 4 page

BOOTSTRAPPING METHOD FOR DEVELOPING PART-OF-SPEECH TAGGED CORPUS IN LOW RESOURCE LANGUAGES TAGSET- A FOCUS ON AN AFRICAN IGBO

In this paper, we demonstrate the efficacy of a POS annotation method that employed the services of two automatic approaches to assist POS tagged corpus creation for a novel language in NLP. The two approaches are cross-lingual and monolingual POS tags projection. We used cross-lingual to automatically create an initial ‘errorful’ tagged corpus for a target language via word-alignment. The resources for creating this are derived from a source language rich in NLP resources. A monolingual method is applied to clean the induce noise via an alignment process and to transform the source language tags to the target language tags. We used English and Igbo as our case study. This is possible because there are parallel texts that exist between English and Igbo, and the source language English has available NLP resources. The results of the experiment show a steady improvement in accuracy and rate of tags transformation with score ranges of 6.13% to 83.79% and 8.67% to 98.37% respectively. The rate of tags transformation evaluates the rate at which source language tags are translated to target language tags

Combining ontologies and neural networks for analyzing historical language varieties: a case study in Middle Low German

In this paper, we describe experiments on the morphosyntactic annotation of historical language varieties for the example of Middle Low German (MLG), the official language of the German Hanse during the Middle Ages and a dominant language around the Baltic Sea by the time. To our best knowledge, this is the first experiment in automatically producing morphosyntactic annotations for Middle Low German, and accordingly, no part-of-speech (POS) tagset is currently agreed upon. In our experiment, we illustrate how ontology-based specifications of projected annotations can be employed to circumvent this issue: Instead of training and evaluating against a given tagset, we decomponse it into independent features which are predicted independently by a neural network. Using consistency constraints (axioms) from an ontology, then, the predicted feature probabilities are decoded into a sound ontological representation. Using these representations, we can finally bootstrap a POS tagset capturing only morphosyntactic features which could be reliably predicted. In this way, our approach is capable to optimize precision and recall of morphosyntactic annotations simultaneously with bootstrapping a tagset rather than performing iterative cycles

Ensemble Morphosyntactic Analyser for Classical Arabic

Classical Arabic (CA) is an influential language for Muslim lives around the world. It is the language of two sources of Islamic laws: the Quran and the Sunnah, the collection of traditions and sayings attributed to the prophet Mohammed. However, classical Arabic in general, and the Sunnah, in particular, is underexplored and under-resourced in the field of computational linguistics. This study examines the possible directions for adapting existing tools, specifically morphological analysers, designed for modern standard Arabic (MSA) to classical Arabic. Morphological analysers of CA are limited, as well as the data for evaluating them. In this study, we adapt existing analysers and create a validation data-set from the Sunnah books. Inspired by the advances in deep learning and the promising results of ensemble methods, we developed a systematic method for transferring morphological analysis that is capable of handling different labelling systems and various sequence lengths. In this study, we handpicked the best four open access MSA morphological analysers. Data generated from these analysers are evaluated before and after adaptation through the existing Quranic Corpus and the Sunnah Arabic Corpus. The findings are as follows: first, it is feasible to analyse under-resourced languages using existing comparable language resources given a small sufficient set of annotated text. Second, analysers typically generate different errors and this could be exploited. Third, an explicit alignment of sequences and the mapping of labels is not necessary to achieve comparable accuracies given a sufficient size of training dataset. Adapting existing tools is easier than creating tools from scratch. The resulting quality is dependent on training data size and number and quality of input taggers. Pipeline architecture performs less well than the End-to-End neural network architecture due to error propagation and limitation on the output format. A valuable tool and data for annotating classical Arabic is made freely available

D4.1. Technologies and tools for corpus creation, normalization and annotation

The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition

Part of Speech Tagging and Lemmatisation for the Spoken Dutch Corpus

Abstract This paper describes the lemmatisation and tagging guidelines developed for the "Spoken Dutch Corpus", and lays out the philosophy behind the high granularity tagset that was designed for the project. To bootstrap the annotation of large quantities of material (10 million words) with this new tagset we tested several existing taggers and tagger generators on initial samples of the corpus. The results show that the most effective method, when trained on the small samples, is a high quality implementation of a Hidden Markov Model tagger generator