Timothy Miller

Also published as: Tim Miller


2021

pdf bib
Domain adaptation in practice: Lessons from a real-world information extraction pipeline
Timothy Miller | Egoitz Laparra | Steven Bethard
Proceedings of the Second Workshop on Domain Adaptation for NLP

Advances in transfer learning and domain adaptation have raised hopes that once-challenging NLP tasks are ready to be put to use for sophisticated information extraction needs. In this work, we describe an effort to do just that – combining state-of-the-art neural methods for negation detection, document time relation extraction, and aspectual link prediction, with the eventual goal of extracting drug timelines from electronic health record text. We train on the THYME colon cancer corpus and test on both the THYME brain cancer corpus and an internal corpus, and show that performance of the combined systems is unacceptable despite good performance of individual systems. Although domain adaptation shows improvements on each individual system, the model selection problem is a barrier to improving overall pipeline performance.

pdf bib
Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition
Lifeng Jin | Lane Schwartz | Finale Doshi-Velez | Timothy Miller | William Schuler
Computational Linguistics, Volume 47, Issue 1 - March 2021

Abstract This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech. The article then explores the idea that the difference between simple grammars exhibited by child learners and fully recursive grammars exhibited by adult learners may be an effect of increasing working memory capacity, where the shallow grammars are constrained images of the recursive grammars. An implementation of these memory bounds as limits on center embedding in a depth-specific transform of a recursive grammar yields a significant improvement over an equivalent but unbounded baseline, suggesting that this arrangement may indeed confer a learning advantage.

pdf bib
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
Egoitz Laparra | Xin Su | Yiyun Zhao | Özlem Uzuner | Timothy Miller | Steven Bethard
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021. The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints. Specifically, we consider the scenario where annotations exist for a domain but cannot be shared. Instead, participants are provided with models trained on that (source) data. Participants also receive some labeled data from a new (development) domain on which to explore domain adaptation algorithms. Participants are then tested on data representing a new (target) domain. We explored this scenario with two different semantic tasks: negation detection (a text classification task) and time expression recognition (a sequence tagging task).

pdf bib
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova
Proceedings of the 20th Workshop on Biomedical Language Processing

Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks. However, most models are pretrained on general domain data. We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process. We show that such a model achieves superior results on clinical extraction tasks by comparing our entity-centric masking strategy with classic random masking on three clinical NLP tasks: cross-domain negation detection, document time relation (DocTimeRel) classification, and temporal relation extraction. We also evaluate our models on the PubMedQA dataset to measure the models’ performance on a non-entity-centric task in the biomedical domain. The language addressed in this work is English.

2020

pdf bib
Methods for Extracting Information from Messages from Primary Care Providers to Specialists
Xiyu Ding | Michael Barnett | Ateev Mehrotra | Timothy Miller
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations

Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems. Understanding the usage patterns of eConsult system is an important part of improving specialist efficiency. In this work, we develop and apply classifiers to a dataset of eConsult questions from primary care providers to specialists, classifying the messages for how they were triaged by the specialist office, and the underlying type of clinical question posed by the primary care provider. We show that pre-trained transformer models are strong baselines, with improving performance from domain-specific training and shared representations.

pdf bib
Defining and Learning Refined Temporal Relations in the Clinical Narrative
Kristin Wright-Bettner | Chen Lin | Timothy Miller | Steven Bethard | Dmitriy Dligach | Martha Palmer | James H. Martin | Guergana Savova
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.

pdf bib
A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Farig Sadeque | Steven Bethard | Guergana Savova
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is inefficient as it requires multiple passes through each input sequence. We extend a recently-proposed one-pass model for relation classification to a one-pass model for relation extraction. We augment this framework by introducing global embeddings to help with long-distance relation inference, and by multi-task learning to increase model performance and generalizability. Our proposed model produces results on par with the state-of-the-art in temporal relation extraction on the THYME corpus and is much “greener” in computational cost.

pdf bib
Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries
Xiyu Ding | Mei-Hua Hall | Timothy Miller
Proceedings of the 3rd Clinical Natural Language Processing Workshop

Reducing rates of early hospital readmission has been recognized and identified as a key to improve quality of care and reduce costs. There are a number of risk factors that have been hypothesized to be important for understanding re-admission risk, including such factors as problems with substance abuse, ability to maintain work, relations with family. In this work, we develop Roberta-based models to predict the sentiment of sentences describing readmission risk factors in discharge summaries of patients with psychosis. We improve substantially on previous results by a scheme that shares information across risk factors while also allowing the model to learn risk factor-specific information.

pdf bib
Extracting Relations between Radiotherapy Treatment Details
Danielle Bitterman | Timothy Miller | David Harris | Chen Lin | Sean Finan | Jeremy Warner | Raymond Mak | Guergana Savova
Proceedings of the 3rd Clinical Natural Language Processing Workshop

We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the treatment of most solid cancers. Its details are described in non-standardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction. We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction. The neural systems perform at 0.82-0.88 macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement. To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.

2019

pdf bib
Simplified Neural Unsupervised Domain Adaptation
Timothy Miller
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Unsupervised domain adaptation (UDA) is the task of training a statistical model on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations that are trained to predict the values of subset of important features called “pivot features” on combined data from the source and target domains. In this work, we show that it is possible to improve on existing neural domain adaptation algorithms by 1) jointly training the representation learner with the task learner; and 2) removing the need for heuristically-selected “pivot features.” Our results show competitive performance with a simpler model.

pdf bib
Extracting Adverse Drug Event Information with Minimal Engineering
Timothy Miller | Alon Geva | Dmitriy Dligach
Proceedings of the 2nd Clinical Natural Language Processing Workshop

In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods. We use the 2018 N2C2 shared task data as our gold standard data set for training. We train support vector machine classifiers to detect drug and drug attribute spans, and pair these detected entities as training instances for an SVM relation classifier, with both systems using standard features. We compare to baseline neural methods that use standard contextualized embedding representations for entity and relation extraction. The SVM-based system and a neural system obtain comparable results, with the SVM system doing better on concepts and the neural system performing better on relation extraction tasks. The neural system obtains surprisingly strong results compared to the system based on years of research in developing features for information extraction.

pdf bib
A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova
Proceedings of the 2nd Clinical Natural Language Processing Workshop

Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.

pdf bib
Two-stage Federated Phenotyping and Patient Representation Learning
Dianbo Liu | Dmitriy Dligach | Timothy Miller
Proceedings of the 18th BioNLP Workshop and Shared Task

A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent years for automatic information extraction from medical texts. However, algorithms trained on data from a single healthcare provider are not generalizable and error-prone due to the heterogeneity and uniqueness of medical documents. We develop a two-stage federated natural language processing method that enables utilization of clinical notes from different hospitals or clinics without moving the data, and demonstrate its performance using obesity and comorbities phenotyping as medical task. This approach not only improves the quality of a specific clinical task but also facilitates knowledge progression in the whole healthcare system, which is an essential part of learning health system. To the best of our knowledge, this is the first application of federated machine learning in clinical NLP.

pdf bib
Unsupervised Learning of PCFGs with Normalizing Flow
Lifeng Jin | Finale Doshi-Velez | Timothy Miller | Lane Schwartz | William Schuler
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Unsupervised PCFG inducers hypothesize sets of compact context-free rules as explanations for sentences. PCFG induction not only provides tools for low-resource languages, but also plays an important role in modeling language acquisition (Bannard et al., 2009; Abend et al. 2017). However, current PCFG induction models, using word tokens as input, are unable to incorporate semantics and morphology into induction, and may encounter issues of sparse vocabulary when facing morphologically rich languages. This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information. Linguistically motivated sparsity and categorical distance constraints are imposed on the inducer as regularization. Experiments show that the PCFG induction model with normalizing flow produces grammars with state-of-the-art accuracy on a variety of different languages. Ablation further shows a positive effect of normalizing flow, context embeddings and proposed regularizers.

pdf bib
Cross-document coreference: An approach to capturing coreference without context
Kristin Wright-Bettner | Martha Palmer | Guergana Savova | Piet de Groen | Timothy Miller
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

This paper discusses a cross-document coreference annotation schema that was developed to further automatic extraction of timelines in the clinical domain. Lexical senses and coreference choices are determined largely by context, but cross-document work requires reasoning across contexts that are not necessarily coherent. We found that an annotation approach that relies less on context-guided annotator intuitions and more on schematic rules was most effective in creating meaningful and consistent cross-document relations.

2018

pdf bib
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Chen Lin | Timothy Miller | Dmitriy Dligach | Hadi Amiri | Steven Bethard | Guergana Savova
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance. We propose to build a recurrent neural network with multiple semantically heterogeneous embeddings within a self-training framework. Our framework makes use of labeled, unlabeled, and social media data, operates on basic features, and is scalable and generalizable. With this method, we establish the state-of-the-art result for both in- and cross-domain for a clinical temporal relation extraction task.

pdf bib
Unsupervised Grammar Induction with Depth-bounded PCFG
Lifeng Jin | Finale Doshi-Velez | Timothy Miller | William Schuler | Lane Schwartz
Transactions of the Association for Computational Linguistics, Volume 6

There has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.

pdf bib
Learning Patient Representations from Text
Dmitriy Dligach | Timothy Miller
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping. Phenotyping has numerous applications such as outcome prediction, clinical trial recruitment, and retrospective studies. Supervised machine learning for phenotyping typically relies on sparse patient representations such as bag-of-words. We consider an alternative that involves learning patient representations. We develop a neural network model for learning patient representations and show that the learned representations are general enough to obtain state-of-the-art performance on a standard comorbidity detection task.

pdf bib
Spotting Spurious Data with Neural Networks
Hadi Amiri | Timothy Miller | Guergana Savova
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings. In this paper, we present effective approaches inspired by queueing theory and psychology of learning to automatically identify spurious instances in datasets. Our approaches discriminate instances based on their “difficulty to learn,” determined by a downstream learner. Our methods can be applied to any dataset assuming the existence of a neural network model for the target task of the dataset. Our best approach outperforms competing state-of-the-art baselines and has a MAP of 0.85 and 0.22 in identifying spurious instances in synthetic and carefully-crowdsourced real-world datasets respectively.

pdf bib
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction
Lifeng Jin | Finale Doshi-Velez | Timothy Miller | William Schuler | Lane Schwartz
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

There have been several recent attempts to improve the accuracy of grammar induction systems by bounding the recursive complexity of the induction model. Modern depth-bounded grammar inducers have been shown to be more accurate than early unbounded PCFG inducers, but this technique has never been compared against unbounded induction within the same system, in part because most previous depth-bounding models are built around sequence models, the complexity of which grows exponentially with the maximum allowed depth. The present work instead applies depth bounds within a chart-based Bayesian PCFG inducer, where bounding can be switched on and off, and then samples trees with or without bounding. Results show that depth-bounding is indeed significantly effective in limiting the search space of the inducer and thereby increasing accuracy of resulting parsing model, independent of the contribution of modern Bayesian induction techniques. Moreover, parsing results on English, Chinese and German show that this bounded model is able to produce parse trees more accurately than or competitively with state-of-the-art constituency grammar induction models.

2017

pdf bib
Unsupervised Domain Adaptation for Clinical Negation Detection
Timothy Miller | Steven Bethard | Hadi Amiri | Guergana Savova
BioNLP 2017

Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.

pdf bib
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova
BioNLP 2017

Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.

pdf bib
Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks
Hadi Amiri | Timothy Miller | Guergana Savova
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a novel approach for training artificial neural networks. Our approach is inspired by broad evidence in psychology that shows human learners can learn efficiently and effectively by increasing intervals of time between subsequent reviews of previously learned materials (spaced repetition). We investigate the analogy between training neural models and findings in psychology about human memory model and develop an efficient and effective algorithm to train neural models. The core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their “reviews” are spaced over time. Our algorithm uses only 34-50% of data per epoch, is 2.9-4.8 times faster than standard training, and outperforms competing state-of-the-art baselines. Our code is available at scholar.harvard.edu/hadi/RbF/.

pdf bib
Neural Temporal Relation Extraction
Dmitriy Dligach | Timothy Miller | Chen Lin | Steven Bethard | Guergana Savova
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.

2016

pdf bib
Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence
Ping Tan | Karin Verspoor | Timothy Miller
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

pdf bib
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input
Cory Shain | William Bryce | Lifeng Jin | Victoria Krakovna | Finale Doshi-Velez | Timothy Miller | William Schuler | Lane Schwartz
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM). We deploy this algorithm to shed light on the extent to which human language learners can discover hierarchical syntax through distributional statistics alone, by modeling two widely-accepted features of human language acquisition and sentence processing that have not been simultaneously modeled by any existing grammar induction algorithm: (1) a left-corner parsing strategy and (2) limited working memory capacity. To model realistic input to human language learners, we evaluate our system on a corpus of child-directed speech rather than typical newswire corpora. Results beat or closely match those of three competing systems.

pdf bib
Unsupervised Document Classification with Informed Topic Models
Timothy Miller | Dmitriy Dligach | Guergana Savova
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

pdf bib
Improving Temporal Relation Extraction with Training Instance Augmentation
Chen Lin | Timothy Miller | Dmitriy Dligach | Steven Bethard | Guergana Savova
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

2015

pdf bib
Structural Alignment as the Basis to Improve Significant Change Detection in Versioned Sentences
Ping Ping Tan | Karin Verspoor | Tim Miller
Proceedings of the Australasian Language Technology Association Workshop 2015

pdf bib
Extracting Time Expressions from Clinical Text
Timothy Miller | Steven Bethard | Dmitriy Dligach | Chen Lin | Guergana Savova
Proceedings of BioNLP 15

2014

pdf bib
Temporal Annotation in the Clinical Domain
William F. Styler IV | Steven Bethard | Sean Finan | Martha Palmer | Sameer Pradhan | Piet C de Groen | Brad Erickson | Timothy Miller | Chen Lin | Guergana Savova | James Pustejovsky
Transactions of the Association for Computational Linguistics, Volume 2

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.

pdf bib
Descending-Path Convolution Kernel for Syntactic Structures
Chen Lin | Timothy Miller | Alvin Kho | Steven Bethard | Dmitriy Dligach | Sameer Pradhan | Guergana Savova
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

pdf bib
Discovering Temporal Narrative Containers in Clinical Text
Timothy Miller | Steven Bethard | Dmitriy Dligach | Sameer Pradhan | Chen Lin | Guergana Savova
Proceedings of the 2013 Workshop on Biomedical Natural Language Processing

pdf bib
Active Learning for Phenotyping Tasks
Dmitriy Dligach | Timothy Miller | Guergana Savova
Proceedings of the Workshop on NLP for Medicine and Biology associated with RANLP 2013

2012

pdf bib
Active Learning for Coreference Resolution
Timothy Miller | Dmitriy Dligach | Guergana Savova
BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing

2011

pdf bib
A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models
Dingcheng Li | Tim Miller | William Schuler
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

pdf bib
HHMM Parsing with Limited Parallelism
Tim Miller | William Schuler
Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics

pdf bib
Broad-Coverage Parsing Using Human-Like Memory Constraints
William Schuler | Samir AbdelRahman | Tim Miller | Lane Schwartz
Computational Linguistics, Volume 36, Number 1, March 2010

2009

pdf bib
Improved Syntactic Models for Parsing Speech with Repairs
Tim Miller
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

pdf bib
Parsing Speech Repair without Specialized Grammar Symbols
Tim Miller | Luan Nguyen | William Schuler
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

pdf bib
Word Buffering Models for Improved Speech Repair Parsing
Tim Miller
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

pdf bib
A Unified Syntactic Model for Parsing Fluent and Disfluent Speech
Tim Miller | William Schuler
Proceedings of ACL-08: HLT, Short Papers

pdf bib
A Syntactic Time-Series Model for Parsing Fluent and Disfluent Speech
Tim Miller | William Schuler
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

pdf bib
Toward a Psycholinguistically-Motivated Model of Language Processing
William Schuler | Samir AbdelRahman | Tim Miller | Lane Schwartz
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)