Jennifer Foster


2020

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Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization
Henry Elder | Robert Burke | Alexander O’Connor | Jennifer Foster
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language Generation shared tasks with the goal of exploring approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages. In the 2018 shared task there was very little difference in the absolute performance of systems trained with and without additional, synthetically created data, and a new rule prohibiting the use of synthetic data was introduced for the 2019 shared task. Contrary to the findings of the 2018 shared task, we show, in experiments on the English 2018 dataset, that the use of synthetic data can have a substantial positive effect – an improvement of almost 8 BLEU points for a previously state-of-the-art system. We analyse the effects of synthetic data, and we argue that its use should be encouraged rather than prohibited so that future research efforts continue to explore systems that can take advantage of such data.

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Treebank Embedding Vectors for Out-of-Domain Dependency Parsing
Joachim Wagner | James Barry | Jennifer Foster
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

A recent advance in monolingual dependency parsing is the idea of a treebank embedding vector, which allows all treebanks for a particular language to be used as training data while at the same time allowing the model to prefer training data from one treebank over others and to select the preferred treebank at test time. We build on this idea by 1) introducing a method to predict a treebank vector for sentences that do not come from a treebank used in training, and 2) exploring what happens when we move away from predefined treebank embedding vectors during test time and instead devise tailored interpolations. We show that 1) there are interpolated vectors that are superior to the predefined ones, and 2) treebank vectors can be predicted with sufficient accuracy, for nine out of ten test languages, to match the performance of an oracle approach that knows the most suitable predefined treebank embedding for the test set.

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Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It’s complicated!
Daria Dzendzik | Carl Vogel | Jennifer Foster
Proceedings of the First Workshop on Insights from Negative Results in NLP

In this paper we explore the problem of machine reading comprehension, focusing on the BoolQ dataset of Yes/No questions. We carry out an error analysis of a BERT-based machine reading comprehension model on this dataset, revealing issues such as unstable model behaviour and some noise within the dataset itself. We then experiment with two approaches for integrating information from knowledge graphs: (i) concatenating knowledge graph triples to text passages and (ii) encoding knowledge with a Graph Neural Network. Neither of these approaches show a clear improvement and we hypothesize that this may be due to a combination of inaccuracies in the knowledge graph, imprecision in entity linking, and the models’ inability to capture additional information from knowledge graphs.

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Annotating Verbal MWEs in Irish for the PARSEME Shared Task 1.2
Abigail Walsh | Teresa Lynn | Jennifer Foster
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

This paper describes the creation of two Irish corpora (labelled and unlabelled) for verbal MWEs for inclusion in the PARSEME Shared Task 1.2 on automatic identification of verbal MWEs, and the process of developing verbal MWE categories for Irish. A qualitative analysis on the two corpora is presented, along with discussion of Irish verbal MWEs.

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Improving Document-Level Sentiment Analysis with User and Product Context
Chenyang Lyu | Jennifer Foster | Yvette Graham
Proceedings of the 28th International Conference on Computational Linguistics

Past work that improves document-level sentiment analysis by encoding user and product in- formation has been limited to considering only the text of the current review. We investigate incorporating additional review text available at the time of sentiment prediction that may prove meaningful for guiding prediction. Firstly, we incorporate all available historical review text belonging to the author of the review in question. Secondly, we investigate the inclusion of his- torical reviews associated with the current product (written by other users). We achieve this by explicitly storing representations of reviews written by the same user and about the same product and force the model to memorize all reviews for one particular user and product. Additionally, we drop the hierarchical architecture used in previous work to enable words in the text to directly attend to each other. Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.

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How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue
Henry Elder | Alexander O’Connor | Jennifer Foster
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural Natural Language Generation (NLG) systems are well known for their unreliability. To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability. While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task. The system trained using this approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.

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The ADAPT Enhanced Dependency Parser at the IWPT 2020 Shared Task
James Barry | Joachim Wagner | Jennifer Foster
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

We describe the ADAPT system for the 2020 IWPT Shared Task on parsing enhanced Universal Dependencies in 17 languages. We implement a pipeline approach using UDPipe and UDPipe-future to provide initial levels of annotation. The enhanced dependency graph is either produced by a graph-based semantic dependency parser or is built from the basic tree using a small set of heuristics. Our results show that, for the majority of languages, a semantic dependency parser can be successfully applied to the task of parsing enhanced dependencies. Unfortunately, we did not ensure a connected graph as part of our pipeline approach and our competition submission relied on a last-minute fix to pass the validation script which harmed our official evaluation scores significantly. Our submission ranked eighth in the official evaluation with a macro-averaged coarse ELAS F1 of 67.23 and a treebank average of 67.49. We later implemented our own graph-connecting fix which resulted in a score of 79.53 (language average) or 79.76 (treebank average), which would have placed fourth in the competition evaluation.

2019

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Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study
James Barry | Joachim Wagner | Jennifer Foster
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. It is a crucial component for inducing dependency parsers in low-resource scenarios where no training data for a language exists. Using Faroese as the target language, we compare two approaches using annotation projection: first, projecting from multiple monolingual source models; second, projecting from a single polyglot model which is trained on the combination of all source languages. Furthermore, we reproduce multi-source projection (Tyers et al., 2018), in which dependency trees of multiple sources are combined. Finally, we apply multi-treebank modelling to the projected treebanks, in addition to or alternatively to polyglot modelling on the source side. We find that polyglot training on the source languages produces an overall trend of better results on the target language but the single best result for the target language is obtained by projecting from monolingual source parsing models and then training multi-treebank POS tagging and parsing models on the target side.

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Fact or Factitious? Contextualized Opinion Spam Detection
Stefan Kennedy | Niall Walsh | Kirils Sloka | Andrew McCarren | Jennifer Foster
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews. We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potential of contextualised embeddings for fake review detection, and lay the groundwork for future research in this area.

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Designing a Symbolic Intermediate Representation for Neural Surface Realization
Henry Elder | Jennifer Foster | James Barry | Alexander O’Connor
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation

Generated output from neural NLG systems often contain errors such as hallucination, repetition or contradiction. This work focuses on designing a symbolic intermediate representation to be used in multi-stage neural generation with the intention of reducing the frequency of failed outputs. We show that surface realization from this intermediate representation is of high quality and when the full system is applied to the E2E dataset it outperforms the winner of the E2E challenge. Furthermore, by breaking out the surface realization step from typically end-to-end neural systems, we also provide a framework for non-neural based content selection and planning systems to potentially take advantage of semi-supervised pretraining of neural surface realization models.

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Ilfhocail: A Lexicon of Irish MWEs
Abigail Walsh | Teresa Lynn | Jennifer Foster
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

This paper describes the categorisation of Irish MWEs, and the construction of the first version of a lexicon of Irish MWEs for NLP purposes (Ilfhocail, meaning ‘Multiwords’), collected from a number of resources. For the purposes of quality assurance, 530 entries of this lexicon were examined and manually annotated for POS information and MWE category.

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Is It Dish Washer Safe? Automatically Answering “Yes/No” Questions Using Customer Reviews
Daria Dzendzik | Carl Vogel | Jennifer Foster
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

It has become commonplace for people to share their opinions about all kinds of products by posting reviews online. It has also become commonplace for potential customers to do research about the quality and limitations of these products by posting questions online. We test the extent to which reviews are useful in question-answering by combining two Amazon datasets and focusing our attention on yes/no questions. A manual analysis of 400 cases reveals that the reviews directly contain the answer to the question just over a third of the time. Preliminary reading comprehension experiments with this dataset prove inconclusive, with accuracy in the range 50-66%.

2018

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Sentiment Expression Boundaries in Sentiment Polarity Classification
Rasoul Kaljahi | Jennifer Foster
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis. We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification. Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.

2017

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If You Can’t Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking
Dasha Bogdanova | Jennifer Foster | Daria Dzendzik | Qun Liu
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features. We present a neural network architecture based on a combination of recurrent neural networks that are used to encode questions and answers, and a multilayer perceptron. We show how this approach can be combined with additional features, in particular, the discourse features used by previous research. Our neural approach achieves state-of-the-art performance on a public dataset from Yahoo! Answers and its performance is further improved by incorporating the discourse features. Additionally, we present a new dataset of Ask Ubuntu questions where the hybrid approach also achieves good results.

2016

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This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering
Dasha Bogdanova | Jennifer Foster
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Detecting Opinion Polarities using Kernel Methods
Rasoul Kaljahi | Jennifer Foster
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

We investigate the application of kernel methods to representing both structural and lexical knowledge for predicting polarity of opinions in consumer product review. We introduce any-gram kernels which model lexical information in a significantly faster way than the traditional n-gram features, while capturing all possible orders of n-grams n in a sequence without the need to explicitly present a pre-specified set of such orders. We also present an effective format to represent constituency and dependency structure together with aspect terms and sentiment polarity scores. Furthermore, we modify the traditional tree kernel function to compute the similarity based on word embedding vectors instead of exact string match and present experiments using the new models.

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Part-of-speech Tagging of Code-mixed Social Media Content: Pipeline, Stacking and Joint Modelling
Utsab Barman | Joachim Wagner | Jennifer Foster
Proceedings of the Second Workshop on Computational Approaches to Code Switching

2015

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Foreebank: Syntactic Analysis of Customer Support Forums
Rasoul Kaljahi | Jennifer Foster | Johann Roturier | Corentin Ribeyre | Teresa Lynn | Joseph Le Roux
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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DCU-ADAPT: Learning Edit Operations for Microblog Normalisation with the Generalised Perceptron
Joachim Wagner | Jennifer Foster
Proceedings of the Workshop on Noisy User-generated Text

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DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2
Piyush Arora | Chris Hokamp | Jennifer Foster | Gareth Jones
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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Semantic Role Labelling with minimal resources: Experiments with French
Rasoul Kaljahi | Jennifer Foster | Johann Roturier
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

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DCU: Aspect-based Polarity Classification for SemEval Task 4
Joachim Wagner | Piyush Arora | Santiago Cortes | Utsab Barman | Dasha Bogdanova | Jennifer Foster | Lamia Tounsi
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Code Mixing: A Challenge for Language Identification in the Language of Social Media
Utsab Barman | Amitava Das | Joachim Wagner | Jennifer Foster
Proceedings of the First Workshop on Computational Approaches to Code Switching

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DCU-UVT: Word-Level Language Classification with Code-Mixed Data
Utsab Barman | Joachim Wagner | Grzegorz Chrupała | Jennifer Foster
Proceedings of the First Workshop on Computational Approaches to Code Switching

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Syntax and Semantics in Quality Estimation of Machine Translation
Rasoul Kaljahi | Jennifer Foster | Johann Roturier
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Cross-lingual Transfer Parsing for Low-Resourced Languages: An Irish Case Study
Teresa Lynn | Jennifer Foster | Mark Dras | Lamia Tounsi
Proceedings of the First Celtic Language Technology Workshop

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Quality Estimation of English-French Machine Translation: A Detailed Study of the Role of Syntax
Rasoul Kaljahi | Jennifer Foster | Johann Roturier | Raphael Rubino
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization
Joseph Le Roux | Antoine Rozenknop | Jennifer Foster
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Parser Accuracy in Quality Estimation of Machine Translation: A Tree Kernel Approach
Rasoul Samad Zadeh Kaljahi | Jennifer Foster | Raphael Rubino | Johann Roturier | Fred Hollowood
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Estimating the Quality of Translated User-Generated Content
Raphael Rubino | Jennifer Foster | Rasoul Samad Zadeh Kaljahi | Johann Roturier | Fred Hollowood
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Sentiment Analysis of Political Tweets: Towards an Accurate Classifier
Akshat Bakliwal | Jennifer Foster | Jennifer van der Puil | Ron O’Brien | Lamia Tounsi | Mark Hughes
Proceedings of the Workshop on Language Analysis in Social Media

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DCU-Symantec at the WMT 2013 Quality Estimation Shared Task
Raphael Rubino | Joachim Wagner | Jennifer Foster | Johann Roturier | Rasoul Samad Zadeh Kaljahi | Fred Hollowood
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Working with a small dataset - semi-supervised dependency parsing for Irish
Teresa Lynn | Jennifer Foster | Mark Dras
Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages

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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

2012

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Active Learning and the Irish Treebank
Teresa Lynn | Jennifer Foster | Mark Dras | Elaine Uí Dhonnchadha
Proceedings of the Australasian Language Technology Association Workshop 2012

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Identifying High-Impact Sub-Structures for Convolution Kernels in Document-level Sentiment Classification
Zhaopeng Tu | Yifan He | Jennifer Foster | Josef van Genabith | Qun Liu | Shouxun Lin
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Irish Treebanking and Parsing: A Preliminary Evaluation
Teresa Lynn | Özlem Çetinoğlu | Jennifer Foster | Elaine Uí Dhonnchadha | Mark Dras | Josef van Genabith
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Language resources are essential for linguistic research and the development of NLP applications. Low-density languages, such as Irish, therefore lack significant research in this area. This paper describes the early stages in the development of new language resources for Irish ― namely the first Irish dependency treebank and the first Irish statistical dependency parser. We present the methodology behind building our new treebank and the steps we take to leverage upon the few existing resources. We discuss language-specific choices made when defining our dependency labelling scheme, and describe interesting Irish language characteristics such as prepositional attachment, copula, and clefting. We manually develop a small treebank of 300 sentences based on an existing POS-tagged corpus and report an inter-annotator agreement of 0.7902. We train MaltParser to achieve preliminary parsing results for Irish and describe a bootstrapping approach for further stages of development.

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DCU-Symantec Submission for the WMT 2012 Quality Estimation Task
Raphael Rubino | Jennifer Foster | Joachim Wagner | Johann Roturier | Rasul Samad Zadeh Kaljahi | Fred Hollowood
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages
Marianna Apidianaki | Ido Dagan | Jennifer Foster | Yuval Marton | Djamé Seddah | Reut Tsarfaty
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

2011

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Decreasing Lexical Data Sparsity in Statistical Syntactic Parsing - Experiments with Named Entities
Deirdre Hogan | Jennifer Foster | Josef van Genabith
Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World

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Comparing the Use of Edited and Unedited Text in Parser Self-Training
Jennifer Foster | Özlem Çetinoğlu | Joachim Wagner | Josef van Genabith
Proceedings of the 12th International Conference on Parsing Technologies

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Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
Djamé Seddah | Reut Tsarfaty | Jennifer Foster
Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages

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From News to Comment: Resources and Benchmarks for Parsing the Language of Web 2.0
Jennifer Foster | Özlem Çetinoğlu | Joachim Wagner | Joseph Le Roux | Joakim Nivre | Deirdre Hogan | Josef van Genabith
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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“cba to check the spelling”: Investigating Parser Performance on Discussion Forum Posts
Jennifer Foster
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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Handling Unknown Words in Statistical Latent-Variable Parsing Models for Arabic, English and French
Mohammed Attia | Jennifer Foster | Deirdre Hogan | Joseph Le Roux | Lamia Tounsi | Josef van Genabith
Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages

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Using Parse Features for Preposition Selection and Error Detection
Joel Tetreault | Jennifer Foster | Martin Chodorow
Proceedings of the ACL 2010 Conference Short Papers

2009

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GenERRate: Generating Errors for Use in Grammatical Error Detection
Jennifer Foster | Oistein Andersen
Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications

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The effect of correcting grammatical errors on parse probabilities
Joachim Wagner | Jennifer Foster
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2008

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Adapting a WSJ-Trained Parser to Grammatically Noisy Text
Jennifer Foster | Joachim Wagner | Josef van Genabith
Proceedings of ACL-08: HLT, Short Papers

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Parser Evaluation and the BNC: Evaluating 4 constituency parsers with 3 metrics
Jennifer Foster | Josef van Genabith
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We evaluate discriminative parse reranking and parser self-training on a new English test set using four versions of the Charniak parser and a variety of parser evaluation metrics. The new test set consists of 1,000 hand-corrected British National Corpus parse trees. We directly evaluate parser output using both the Parseval and the Leaf Ancestor metrics. We also convert the hand-corrected and parser output phrase structure trees to dependency trees using a state-of-the-art functional tag labeller and constituent-to-dependency conversion tool, and then calculate label accuracy, unlabelled attachment and labelled attachment scores over the dependency structures. We find that reranking leads to a performance improvement on the new test set (albeit a modest one). We find that self-training using BNC data leads to significantly better results. However, it is not clear how effective self-training is when the training material comes from the North American News Corpus.

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Parser-Based Retraining for Domain Adaptation of Probabilistic Generators
Deirdre Hogan | Jennifer Foster | Joachim Wagner | Josef van Genabith
Proceedings of the Fifth International Natural Language Generation Conference

2007

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Adapting WSJ-Trained Parsers to the British National Corpus using In-Domain Self-Training
Jennifer Foster | Joachim Wagner | Djamé Seddah | Josef van Genabith
Proceedings of the Tenth International Conference on Parsing Technologies

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A Comparative Evaluation of Deep and Shallow Approaches to the Automatic Detection of Common Grammatical Errors
Joachim Wagner | Jennifer Foster | Josef van Genabith
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2004

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Parsing Ungrammatical Input: an Evaluation Procedure
Jennifer Foster
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)