GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

Learning Chinese language structures with multiple views

Motivated by the inadequacy of single view approaches in many areas in NLP, we study multi-view Chinese language processing, including word segmentation, part-of-speech (POS) tagging, syntactic parsing and semantic role labeling (SRL), in this thesis. We consider three situations of multiple views in statistical NLP: (1) Heterogeneous computational models have been designed for a given problem; (2) Heterogeneous annotation data is available to train systems; (3) Supervised and unsupervised machine learning techniques are applicable. First, we comparatively analyze successful single view approaches for Chinese lexical, syntactic and semantic processing. Our analysis highlights the diversity between heterogenous systems built on different views, and motivates us to improve the state-of-the-art by combining or integrating heterogeneous approaches. Second, we study the annotation ensemble problem, i.e. learning from multiple data sets under different annotation standards. We propose a series of generalized stacking models to effectively utilize heterogeneous labeled data to reduce approximation errors for word segmentation and parsing. Finally, we are concerned with bridging the gap between unsupervised and supervised learning paradigms. We introduce feature induction solutions that harvest useful linguistic knowledge from large-scale unlabeled data and effectively use them as new features to enhance discriminative learning based systems. For word segmentation, we present a comparative study of word-based and character-based approaches. Inspired by the diversity of the two views, we design a novel stacked sub-word tagging model for joint word segmentation and POS tagging, which is robust to integrate different models, even models trained on heterogeneous annotations. To benefit from unsupervised word segmentation, we derive expressive string knowledge from unlabeled data which significantly enhances a strong supervised segmenter. For POS tagging, we introduce two linguistically motivated improvements: (1) combining syntax-free sequential tagging and syntax-based chart parsing results to better capture syntagmatic lexical relations and (2) integrating word clusters acquired from unlabeled data to better capture paradigmatic lexical relations. For syntactic parsing, we present a comparative analysis for generative PCFG-LA constituency parsing and discriminative graph-based dependency parsing. To benefit from the diversity of parsing in different formalisms, we implement a previously introduced stacking method and propose a novel Bagging model to combine complementary strengths of grammar-free and grammar-based models. In addition to the study on the syntactic formalism, we also propose a reranking model to explore heterogenous treebanks that are labeled under different annotation scheme. Finally, we continue our efforts on combining strengths of supervised and unsupervised learning, and evaluate the impact of word clustering on different syntactic processing tasks. Our work on SRL focus on improving the full parsing method with linguistically rich features and a chunking strategy. Furthermore, we developed a partial parsing based semantic chunking method, which has complementary strengths to the full parsing based method. Based on our work, Zhuang and Zong (2010) successfully improve the state-of-the-art by combining full and partial parsing based SRL systems.Motiviert durch die Unzulänglichkeit der Ansätze mit dem einzigen Ansicht in vielen Bereichen in NLP, untersuchen wir Chinesische Sprache Verarbeitung mit mehrfachen Ansichten, einschließlich Wortsegmentierung, Part-of-Speech (POS)-Tagging und syntaktische Parsing und die Kennzeichnung der semantische Rolle (SRL) in dieser Arbeit . Wir betrachten drei Situationen von mehreren Ansichten in der statistischen NLP: (1) Heterogene computergestützte Modelle sind für ein gegebenes Problem entwurft, (2) Heterogene Annotationsdaten sind verfügbar, um die Systeme zu trainieren, (3) überwachten und unüberwachten Methoden des maschinellen Lernens sind zur Verfügung gestellt. Erstens, wir analysieren vergleichsweise erfolgreiche Ansätze mit einzigen Ansicht für chinesische lexikalische, syntaktische und semantische Verarbeitung. Unsere Analyse zeigt die Unterschiede zwischen den heterogenen Systemen, die auf verschiedenen Ansichten gebaut werden, und motiviert uns, die state-of-the-Art durch die Kombination oder Integration heterogener Ansätze zu verbessern. Zweitens, untersuchen wir die Annotation Ensemble Problem, d.h. das Lernen aus mehreren Datensätzen unter verschiedenen Annotation Standards. Wir schlagen eine Reihe allgemeiner Stapeln Modelle, um eine effektive Nutzung heterogener Daten zu beschriften, und um Approximationsfehler für Wort Segmentierung und Analyse zu reduzieren. Schließlich sind wir besorgt mit der Überbrückung der Kluft zwischen unüberwachten und überwachten Lernens Paradigmen. Wir führen Induktion Feature-Lösungen, die nützliche Sprachkenntnisse von großflächigen unmarkierter Daten ernte, und die effektiv nutzen als neue Features, um die unterscheidenden Lernen basierten Systemen zu verbessern. Für die Wortsegmentierung, präsentieren wir eine vergleichende Studie der Wort-basierte und Charakter-basierten Ansätzen. Inspiriert von der Vielfalt der beiden Ansichten, entwerfen wir eine neuartige gestapelt Sub-Wort-Tagging-Modell für gemeinsame Wort-Segmentierung und POS-Tagging, die robust ist, um verschiedene Modelle zu integrieren, auch Modelle auf heterogenen Annotationen geschult. Um den unbeaufsichtigten Wortsegmentierung zu profitieren, leiten wir ausdrucksstarke Zeichenfolge Wissen von unmarkierten Daten. Diese Methode hat eine überwachte Methode erheblich verbessert. Für POS-Tagging, führen wir zwei linguistisch motiviert Verbesserungen: (1) die Kombination von Syntaxfreie sequentielle Tagging und Syntaxbasierten Grafik-Parsing-Ergebnisse, um syntagmatische lexikalische Beziehungen besser zu erfassen (2) die Integration von Wortclusteren von nicht markierte Daten, um die paradigmatische lexikalische Beziehungen besser zu erfassen. Für syntaktische Parsing präsentieren wir eine vergleichenbare Analyse für generative PCFG-LA Wahlkreis Parsing und diskriminierende Graphen-basierte Abhängigkeit Parsing. Um aus der Vielfalt der Parsen in unterschiedlichen Formalismen zu profitieren, setzen wir eine zuvor eingeführte Stacking-Methode und schlagen eine neuartige Schrumpfbeutel-Modell vor, um die ergänzenden Stärken der Grammatik und Grammatik-free-basierte Modelle zu kombinieren. Neben dem syntaktischen Formalismus, wir schlagen auch ein Modell, um heterogene reranking Baumbanken, die unter verschiedenen Annotationsschema beschriftet sind zu erkunden. Schließlich setzen wir unsere Bemühungen auf die Bündelung von Stärken des überwachten und unüberwachten Lernen, und bewerten wir die Auswirkungen der Wort-Clustering auf verschiedene syntaktische Verarbeitung Aufgaben. Unsere Arbeit an SRL ist konzentriert auf die Verbesserung der vollen Parsingsmethode mit linguistischen umfangreichen Funktionen und einer Chunkingstrategie. Weiterhin entwickelten wir eine semantische Chunkingmethode basiert auf dem partiellen Parsing, die die komplementäre Stärken gegen die die Methode basiert auf dem vollen Parsing hat. Basiert auf unserer Arbeit, Zhuang und Zong (2010) hat den aktuelle Stand erfolgreich verbessert durch die Kombination von voll-und partielle-Parsing basierte SRL Systeme

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

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015