Sandra Kübler

Also published as: Sandra Kubler, Sandra Kuebler


2021

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Bidirectional Domain Adaptation Using Weighted Multi-Task Learning
Daniel Dakota | Zeeshan Ali Sayyed | Sandra Kübler
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

Domain adaption in syntactic parsing is still a significant challenge. We address the issue of data imbalance between the in-domain and out-of-domain treebank typically used for the problem. We define domain adaptation as a Multi-task learning (MTL) problem, which allows us to train two parsers, one for each do-main. Our results show that the MTL approach is beneficial for the smaller treebank. For the larger treebank, we need to use loss weighting in order to avoid a decrease in performance be-low the single task. In order to determine towhat degree the data imbalance between two domains and the domain differences affect results, we also carry out an experiment with two imbalanced in-domain treebanks and show that loss weighting also improves performance in an in-domain setting. Given loss weighting in MTL, we can improve results for both parsers.

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What’s in a Span? Evaluating the Creativity of a Span-Based Neural Constituency Parser
Daniel Dakota | Sandra Kübler
Proceedings of the Society for Computation in Linguistics 2021

2020

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Offensive Language Detection Using Brown Clustering
Zuoyu Tian | Sandra Kübler
Proceedings of the 12th Language Resources and Evaluation Conference

In this study, we investigate the use of Brown clustering for offensive language detection. Brown clustering has been shown to be of little use when the task involves distinguishing word polarity in sentiment analysis tasks. In contrast to previous work, we train Brown clusters separately on positive and negative sentiment data, but then combine the information into a single complex feature per word. This way of representing words results in stable improvements in offensive language detection, when used as the only features or in combination with words or character n-grams. Brown clusters add important information, even when combined with words or character n-grams or with standard word embeddings in a convolutional neural network. However, we also found different trends between the two offensive language data sets we used.

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OCNLI: Original Chinese Natural Language Inference
Hai Hu | Kyle Richardson | Liang Xu | Lu Li | Sandra Kübler | Lawrence Moss
Findings of the Association for Computational Linguistics: EMNLP 2020

Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g.,SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a lack of reliable datasets for most of the world’s languages. In this paper, we present the first large-scale NLI dataset (consisting of ~56,000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI). Unlike recent attempts at extending NLI to other languages, our dataset does not rely on any automatic translation or non-expert annotation. Instead, we elicit annotations from native speakers specializing in linguistics. We follow closely the annotation protocol used for MNLI, but create new strategies for eliciting diverse hypotheses. We establish several baseline results on our dataset using state-of-the-art pre-trained models for Chinese, and find even the best performing models to be far outpaced by human performance (~12% absolute performance gap), making it a challenging new resource that we hope will help to accelerate progress in Chinese NLU. To the best of our knowledge, this is the first human-elicited MNLI-style corpus for a non-English language.

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MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
Hai Hu | Qi Chen | Kyle Richardson | Atreyee Mukherjee | Lawrence S. Moss | Sandra Kuebler
Proceedings of the Society for Computation in Linguistics 2020

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Building a Treebank for Chinese Literature for Translation Studies
Hai Hu | Yanting Li | Yina Patterson | Zuoyu Tian | Yiwen Zhang | He Zhou | Sandra Kuebler | Chien-Jer Charles Lin
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

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Fine-Grained Morpho-Syntactic Analysis for the Under-Resourced Language Chaghatay
Kenneth Steimel | Akbar Amat | Arienne Dwyer | Sandra Kübler
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

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Universal Dependency Treebank for Xibe
He Zhou | Juyeon Chung | Sandra Kübler | Francis Tyers
Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)

We present our work of constructing the first treebank for the Xibe language following the Universal Dependencies (UD) annotation scheme. Xibe is a low-resourced and severely endangered Tungusic language spoken by the Xibe minority living in the Xinjiang Uygur Autonomous Region of China. We collected 810 sentences so far, including 544 sentences from a grammar book on written Xibe and 266 sentences from Cabcal News. We annotated those sentences manually from scratch. In this paper, we report the procedure of building this treebank and analyze several important annotation issues of our treebank. Finally, we propose our plans for future work.

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Investigating Sampling Bias in Abusive Language Detection
Dante Razo | Sandra Kübler
Proceedings of the Fourth Workshop on Online Abuse and Harms

Abusive language detection is becoming increasingly important, but we still understand little about the biases in our datasets for abusive language detection, and how these biases affect the quality of abusive language detection. In the work reported here, we reproduce the investigation of Wiegand et al. (2019) to determine differences between different sampling strategies. They compared boosted random sampling, where abusive posts are upsampled, and biased topic sampling, which focuses on topics that are known to cause abusive language. Instead of comparing individual datasets created using these sampling strategies, we use the sampling strategies on a single, large dataset, thus eliminating the textual source of the dataset as a potential confounding factor. We show that differences in the textual source can have more effect than the chosen sampling strategy.

2019

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UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs
Jian Zhu | Zuoyu Tian | Sandra Kübler
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the UM-IU@LING’s system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.

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Investigating Multilingual Abusive Language Detection: A Cautionary Tale
Kenneth Steimel | Daniel Dakota | Yue Chen | Sandra Kübler
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Abusive language detection has received much attention in the last years, and recent approaches perform the task in a number of different languages. We investigate which factors have an effect on multilingual settings, focusing on the compatibility of data and annotations. In the current paper, we focus on English and German. Our findings show large differences in performance between the two languages. We find that the best performance is achieved by different classification algorithms. Sampling to address class imbalance issues is detrimental for German and beneficial for English. The only similarity that we find is that neither data set shows clear topics when we compare the results of topic modeling to the gold standard. Based on our findings, we can conclude that a multilingual optimization of classifiers is not possible even in settings where comparable data sets are used.

2018

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Practical Parsing for Downstream Applications
Daniel Dakota | Sandra Kübler
Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts

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UniMorph 2.0: Universal Morphology
Christo Kirov | Ryan Cotterell | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Patrick Xia | Manaal Faruqui | Sabrina J. Mielke | Arya McCarthy | Sandra Kübler | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Detecting Syntactic Features of Translated Chinese
Hai Hu | Wen Li | Sandra Kübler
Proceedings of the Second Workshop on Stylistic Variation

We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features. Using Support Vector Machines (SVMs) as classifier on a genre-balanced corpus in translation studies of Chinese, we find that constituent parse trees and dependency triples as features without lexical information perform very well on the task, with an F-measure above 90%, close to the results of lexical n-gram features, without the risk of learning topic information rather than translation features. Thus, we claim syntactic features alone can accurately distinguish translated from original Chinese. Translated Chinese exhibits an increased use of determiners, subject position pronouns, NP + “的” as NP modifiers, multiple NPs or VPs conjoined by "、", among other structures. We also interpret the syntactic features with reference to previous translation studies in Chinese, particularly the usage of pronouns.

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Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology
Sandra Kuebler | Garrett Nicolai
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

2017

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Native Language Identification using Phonetic Algorithms
Charese Smiley | Sandra Kübler
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

In this paper, we discuss the results of the IUCL system in the NLI Shared Task 2017. For our system, we explore a variety of phonetic algorithms to generate features for Native Language Identification. These features are contrasted with one of the most successful type of features in NLI, character n-grams. We find that although phonetic features do not perform as well as character n-grams alone, they do increase overall F1 score when used together with character n-grams.

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CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
Ryan Cotterell | Christo Kirov | John Sylak-Glassman | Géraldine Walther | Ekaterina Vylomova | Patrick Xia | Manaal Faruqui | Sandra Kübler | David Yarowsky | Jason Eisner | Mans Hulden
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection

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Towards Replicability in Parsing
Daniel Dakota | Sandra Kübler
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We investigate parsing replicability across 7 languages (and 8 treebanks), showing that choices concerning the use of grammatical functions in parsing or evaluation, the influence of the rare word threshold, as well as choices in test sentences and evaluation script options have considerable and often unexpected effects on parsing accuracies. All of those choices need to be carefully documented if we want to ensure replicability.

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Non-Deterministic Segmentation for Chinese Lattice Parsing
Hai Hu | Daniel Dakota | Sandra Kübler
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses. We investigate a pipeline approach to segmentation and parsing using word lattices as parser input. We compare CRF-based and lexicon-based approaches to word segmentation. Our results show that the lattice parser is capable of selecting the correction segmentation from thousands of options, thus drastically reducing the number of unparsed sentence. Lexicon-based parsing models have a better coverage than the CRF-based approach, but the many options are more difficult to handle. We reach our best result by using a lexicon from the n-best CRF analyses, combined with highly probable words.

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Similarity Based Genre Identification for POS Tagging Experts & Dependency Parsing
Atreyee Mukherjee | Sandra Kübler
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

POS tagging and dependency parsing achieve good results for homogeneous datasets. However, these tasks are much more difficult on heterogeneous datasets. In (Mukherjee et al. 2016, 2017), we address this issue by creating genre experts for both POS tagging and parsing. We use topic modeling to automatically separate training and test data into genres and to create annotation experts per genre by training separate models for each topic. However, this approach assumes that topic modeling is performed jointly on training and test sentences each time a new test sentence is encountered. We extend this work by assigning new test sentences to their genre expert by using similarity metrics. We investigate three different types of methods: 1) based on words highly associated with a genre by the topic modeler, 2) using a k-nearest neighbor classification approach, and 3) using perplexity to determine the closest topic. The results show that the choice of similarity metric has an effect on results and that we can reach comparable accuracies to the joint topic modeling in POS tagging and dependency parsing, thus providing a viable and efficient approach to POS tagging and parsing a sentence by its genre expert.

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Creating POS Tagging and Dependency Parsing Experts via Topic Modeling
Atreyee Mukherjee | Sandra Kübler | Matthias Scheutz
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Part of speech (POS) taggers and dependency parsers tend to work well on homogeneous datasets but their performance suffers on datasets containing data from different genres. In our current work, we investigate how to create POS tagging and dependency parsing experts for heterogeneous data by employing topic modeling. We create topic models (using Latent Dirichlet Allocation) to determine genres from a heterogeneous dataset and then train an expert for each of the genres. Our results show that the topic modeling experts reach substantial improvements when compared to the general versions. For dependency parsing, the improvement reaches 2 percent points over the full training baseline when we use two topics.

2016

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Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing
Wolfgang Maier | Sandra Kübler | Constantin Orasan
Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing

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Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
Micha Elsner | Sandra Kuebler
Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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POS Tagging Experts via Topic Modeling
Atreyee Mukherjee | Sandra Kübler | Matthias Scheutz
Proceedings of the 13th International Conference on Natural Language Processing

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IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter
Can Liu | Wen Li | Bradford Demarest | Yue Chen | Sara Couture | Daniel Dakota | Nikita Haduong | Noah Kaufman | Andrew Lamont | Manan Pancholi | Kenneth Steimel | Sandra Kübler
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Tools for Digital Humanities: Enabling Access to the Old Occitan Romance of Flamenca
Olga Scrivner | Sandra Kübler
Proceedings of the Fourth Workshop on Computational Linguistics for Literature

2014

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The IUCL+ System: Word-Level Language Identification via Extended Markov Models
Levi King | Eric Baucom | Timur Gilmanov | Sandra Kübler | Dan Whyatt | Wolfgang Maier | Paul Rodrigues
Proceedings of the First Workshop on Computational Approaches to Code Switching

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Feature Selection for Highly Skewed Sentiment Analysis Tasks
Can Liu | Sandra Kübler | Ning Yu
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

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“My Curiosity was Satisfied, but not in a Good Way”: Predicting User Ratings for Online Recipes
Can Liu | Chun Guo | Daniel Dakota | Sridhar Rajagopalan | Wen Li | Sandra Kübler | Ning Yu
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

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Parsing German: How Much Morphology Do We Need?
Wolfgang Maier | Sandra Kübler | Daniel Dakota | Daniel Whyatt
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages

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Introducing the SPMRL 2014 Shared Task on Parsing Morphologically-rich Languages
Djamé Seddah | Sandra Kübler | Reut Tsarfaty
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages

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IUCL: Combining Information Sources for SemEval Task 5
Alex Rudnick | Levi King | Can Liu | Markus Dickinson | Sandra Kübler
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Discosuite - A parser test suite for German discontinuous structures
Wolfgang Maier | Miriam Kaeshammer | Peter Baumann | Sandra Kübler
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Parser evaluation traditionally relies on evaluation metrics which deliver a single aggregate score over all sentences in the parser output, such as PARSEVAL. However, for the evaluation of parser performance concerning a particular phenomenon, a test suite of sentences is needed in which this phenomenon has been identified. In recent years, the parsing of discontinuous structures has received a rising interest. Therefore, in this paper, we present a test suite for testing the performance of dependency and constituency parsers on non-projective dependencies and discontinuous constituents for German. The test suite is based on the newly released TIGER treebank version 2.2. It provides a unique possibility of benchmarking parsers on non-local syntactic relationships in German, for constituents and dependencies. We include a linguistic analysis of the phenomena that cause discontinuity in the TIGER annotation, thereby closing gaps in previous literature. The linguistic phenomena we investigate include extraposition, a placeholder/repeated element construction, topicalization, scrambling, local movement, parentheticals, and fronting of pronouns.

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SWIFT Aligner, A Multifunctional Tool for Parallel Corpora: Visualization, Word Alignment, and (Morpho)-Syntactic Cross-Language Transfer
Timur Gilmanov | Olga Scrivner | Sandra Kübler
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

It is well known that word aligned parallel corpora are valuable linguistic resources. Since many factors affect automatic alignment quality, manual post-editing may be required in some applications. While there are several state-of-the-art word-aligners, such as GIZA++ and Berkeley, there is no simple visual tool that would enable correcting and editing aligned corpora of different formats. We have developed SWIFT Aligner, a free, portable software that allows for visual representation and editing of aligned corpora from several most commonly used formats: TALP, GIZA, and NAACL. In addition, our tool has incorporated part-of-speech and syntactic dependency transfer from an annotated source language into an unannotated target language, by means of word-alignment.

2013

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Does Size Matter? Text and Grammar Revision for Parsing Social Media Data
Mohammad Khan | Markus Dickinson | Sandra Kuebler
Proceedings of the Workshop on Language Analysis in Social Media

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Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
Djamé Seddah | Reut Tsarfaty | Sandra Kübler | Marie Candito | Jinho D. Choi | Richárd Farkas | Jennifer Foster | Iakes Goenaga | Koldo Gojenola Galletebeitia | Yoav Goldberg | Spence Green | Nizar Habash | Marco Kuhlmann | Wolfgang Maier | Joakim Nivre | Adam Przepiórkowski | Ryan Roth | Wolfgang Seeker | Yannick Versley | Veronika Vincze | Marcin Woliński | Alina Wróblewska | Eric Villemonte de la Clergerie
Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages

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ASMA: A System for Automatic Segmentation and Morpho-Syntactic Disambiguation of Modern Standard Arabic
Muhammad Abdul-Mageed | Mona Diab | Sandra Kübler
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Domain Adaptation for Parsing
Eric Baucom | Levi King | Sandra Kübler
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Towards Domain Adaptation for Parsing Web Data
Mohammad Khan | Markus Dickinson | Sandra Kübler
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Machine Learning for Mention Head Detection in Multilingual Coreference Resolution
Desislava Zhekova | Sandra Kübler
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Parsing Morphologically Rich Languages: Introduction to the Special Issue
Reut Tsarfaty | Djamé Seddah | Sandra Kübler | Joakim Nivre
Computational Linguistics, Volume 39, Issue 1 - March 2013

2012

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Predicting Learner Levels for Online Exercises of Hebrew
Markus Dickinson | Sandra Kübler | Anthony Meyer
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Annotating Coordination in the Penn Treebank
Wolfgang Maier | Sandra Kübler | Erhard Hinrichs | Julia Krivanek
Proceedings of the Sixth Linguistic Annotation Workshop

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SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media
Muhammad Abdul-Mageed | Sandra Kuebler | Mona Diab
Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis

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UBIU for Multilingual Coreference Resolution in OntoNotes
Desislava Zhekova | Sandra Kübler | Joshua Bonner | Marwa Ragheb | Yu-Yin Hsu
Joint Conference on EMNLP and CoNLL - Shared Task

2011

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Filling the Gap: Semi-Supervised Learning for Opinion Detection Across Domains
Ning Yu | Sandra Kübler
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

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UBIU: A Robust System for Resolving Unrestricted Coreference
Desislava Zhekova | Sandra Kübler
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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Towards a Malay Derivational Lexicon: Learning Affixes Using Expectation Maximization
Suriani Sulaiman | Michael Gasser | Sandra Kuebler
Proceedings of the 2nd Workshop on South Southeast Asian Natural Language Processing (WSSANLP)

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Fast Domain Adaptation for Part of Speech Tagging for Dialogues
Sandra Kübler | Eric Baucom
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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Actions Speak Louder than Words: Evaluating Parsers in the Context of Natural Language Understanding Systems for Human-Robot Interaction
Sandra Kübler | Rachael Cantrell | Matthias Scheutz
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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Singletons and Coreference Resolution Evaluation
Sandra Kübler | Desislava Zhekova
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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UBIU: A Language-Independent System for Coreference Resolution
Desislava Zhekova | Sandra Kübler
Proceedings of the 5th International Workshop on Semantic Evaluation

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Is Arabic Part of Speech Tagging Feasible Without Word Segmentation?
Emad Mohamed | Sandra Kübler
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Statistical Parsing of Morphologically Rich Languages (SPMRL) What, How and Whither
Reut Tsarfaty | Djamé Seddah | Yoav Goldberg | Sandra Kuebler | Yannick Versley | Marie Candito | Jennifer Foster | Ines Rehbein | Lamia Tounsi
Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages

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Chunking German: An Unsolved Problem
Sandra Kübler | Kathrin Beck | Erhard Hinrichs | Heike Telljohann
Proceedings of the Fourth Linguistic Annotation Workshop

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Proceedings of the ACL 2010 System Demonstrations
Sandra Kübler
Proceedings of the ACL 2010 System Demonstrations

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Arabic Part of Speech Tagging
Emad Mohamed | Sandra Kübler
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Arabic is a morphologically rich language, which presents a challenge for part of speech tagging. In this paper, we compare two novel methods for POS tagging of Arabic without the use of gold standard word segmentation but with the full POS tagset of the Penn Arabic Treebank. The first approach uses complex tags that describe full words and does not require any word segmentation. The second approach is segmentation-based, using a machine learning segmenter. In this approach, the words are first segmented, then the segments are annotated with POS tags. Because of the word-based approach, we evaluate full word accuracy rather than segment accuracy. Word-based POS tagging yields better results than segment-based tagging (93.93% vs. 93.41%). Word based tagging also gives the best results on known words, the segmentation-based approach gives better results on unknown words. Combining both methods results in a word accuracy of 94.37%, which is very close to the result obtained by using gold standard segmentation (94.91%).

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The Indiana “Cooperative Remote Search Task” (CReST) Corpus
Kathleen Eberhard | Hannele Nicholson | Sandra Kübler | Susan Gundersen | Matthias Scheutz
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper introduces a novel corpus of natural language dialogues obtained from humans performing a cooperative, remote, search task (CReST) as it occurs naturally in a variety of scenarios (e.g., search and rescue missions in disaster areas). This corpus is unique in that it involves remote collaborations between two interlocutors who each have to perform tasks that require the other's assistance. In addition, one interlocutor's tasks require physical movement through an indoor environment as well as interactions with physical objects within the environment. The multi-modal corpus contains the speech signals as well as transcriptions of the dialogues, which are additionally annotated for dialog structure, disfluencies, and for constituent and dependency syntax. On the dialogue level, the corpus was annotated for separate dialogue moves, based on the classification developed by Carletta et al. (1997) for coding task-oriented dialogues. Disfluencies were annotated using the scheme developed by Lickley (1998). The syntactic annotation comprises POS annotation, Penn Treebank style constituent annotations as well as dependency annotations based on the dependencies of pennconverter.

2009

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Parsing Coordinations
Sandra Kübler | Erhard Hinrichs | Wolfgang Maier | Eva Klett
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity
Sandra Kübler | Desislava Zhekova
Proceedings of the International Conference RANLP-2009

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Diacritization for Real-World Arabic Texts
Emad Mohamed | Sandra Kübler
Proceedings of the International Conference RANLP-2009

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Instance Sampling Methods for Pronoun Resolution
Holger Wunsch | Sandra Kübler | Rachael Cantrell
Proceedings of the International Conference RANLP-2009

2008

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Proceedings of the Workshop on Parsing German
Sandra Kübler | Gerald Penn
Proceedings of the Workshop on Parsing German

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The PaGe 2008 Shared Task on Parsing German
Sandra Kübler
Proceedings of the Workshop on Parsing German

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Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Johan Bos | Edward Briscoe | Aoife Cahill | John Carroll | Stephen Clark | Ann Copestake | Dan Flickinger | Josef van Genabith | Julia Hockenmaier | Aravind Joshi | Ronald Kaplan | Tracy Holloway King | Sandra Kuebler | Dekang Lin | Jan Tore Lønning | Christopher Manning | Yusuke Miyao | Joakim Nivre | Stephan Oepen | Kenji Sagae | Nianwen Xue | Yi Zhang
Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation

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How to Compare Treebanks
Sandra Kübler | Wolfgang Maier | Ines Rehbein | Yannick Versley
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Recent years have seen an increasing interest in developing standards for linguistic annotation, with a focus on the interoperability of the resources. This effort, however, requires a profound knowledge of the advantages and disadvantages of linguistic annotation schemes in order to avoid importing the flaws and weaknesses of existing encoding schemes into the new standards. This paper addresses the question how to compare syntactically annotated corpora and gain insights into the usefulness of specific design decisions. We present an exhaustive evaluation of two German treebanks with crucially different encoding schemes. We evaluate three different parsers trained on the two treebanks and compare results using EvalB, the Leaf-Ancestor metric, and a dependency-based evaluation. Furthermore, we present TePaCoC, a new testsuite for the evaluation of parsers on complex German grammatical constructions. The testsuite provides a well thought-out error classification, which enables us to compare parser output for parsers trained on treebanks with different encoding schemes and provides interesting insights into the impact of treebank annotation schemes on specific constructions like PP attachment or non-constituent coordination.

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POS Tagging for German: how important is the Right Context?
Steliana Ivanova | Sandra Kuebler
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of right context for correct disambiguation. We show for German that the best results are reached by a combination of left and right context. If only left context is available, then changing the direction of analysis and going from right to left improves the results. In a version of MBT with default parameter settings, the inclusion of the right context improved POS tagging accuracy from 94.00% to 96.08%, thus corroborating our hypothesis. The version with optimized parameters reaches 96.73%.

2007

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The CoNLL 2007 Shared Task on Dependency Parsing
Joakim Nivre | Johan Hall | Sandra Kübler | Ryan McDonald | Jens Nilsson | Sebastian Riedel | Deniz Yuret
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Book Review: Memory-Based Language Processing, by Walter Daelemans and Antal van den Bosch
Sandra Kübler
Computational Linguistics, Volume 32, Number 4, December 2006

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Towards Case-Based Parsing: Are Chunks Reliable Indicators for Syntax Trees?
Sandra Kübler
Proceedings of the Workshop on Linguistic Distances

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Is it Really that Difficult to Parse German?
Sandra Kübler | Erhard W. Hinrichs | Wolfgang Maier
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2005

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A Unified Representation for Morphological, Syntactic, Semantic, and Referential Annotations
Erhard W. Hinrichs | Sandra Kübler | Karin Naumann
Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky

2004

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The Tüba-D/Z Treebank: Annotating German with a Context-Free Backbone
Heike Telljohann | Erhard Hinrichs | Sandra Kübler
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2002

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A Hybrid Architecture for Robust Parsing of German
Erhard W. Hinrichs | Sandra Kübler | Frank H. Müller | Tylman Ule
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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From Chunks to Function-Argument Structure: A Similarity-Based Approach
Sandra Kübler | Erhard W. Hinrichs
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

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TüSBL: A Similarity-Based Chunk Parser for Robust Syntactic Processing
Sandra Kübler | Erhard W. Hinrichs
Proceedings of the First International Conference on Human Language Technology Research

1998

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Learning a Lexicalized Grammar for German
Sandra Kubler
New Methods in Language Processing and Computational Natural Language Learning

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