The 2017 Shared Task on Extrinsic Parser Evaluation Towards a Reusable Community Infrastructure

The 2017 Shared Task on Extrinsic Parser Evaluation (EPE 2017) seeks to provide better estimates of the relative utility of different types of dependency representations for a variety of downstream applications that depend centrally on the analysis of grammatical structure. EPE 2017 defines a generalized notion of lexicalized syntactico-semantic dependency representations and provides a common interchange format to three state-of-the-art downstream applications, viz. biomedical event extraction, negation resolution, and fine-grained opinion analysis. As a first step towards building a generic and extensible infrastructure for extrinsic parser evaluation, the downstream applications have been generalized to support a broad range of diverese dependency representations (including divergent sentence and token boundaries) and to allow fully automated re-training and evaluation for a specific collection of parser outputs. Nine teams participated in EPE 2017, submitting 49 distinct runs that encompass many different families of dependency representations, distinct approaches to preprocessing and parsing, and various types and volumes of training data.

[1]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[2]  David M. Magerman Statistical Decision-Tree Models for Parsing , 1995, ACL.

[3]  Michael Collins,et al.  Head-Driven Statistical Models for Natural Language Parsing , 2003, CL.

[4]  Eugene Charniak,et al.  Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking , 2005, ACL.

[5]  Claire Cardie,et al.  Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.

[6]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

[7]  Richard Johansson,et al.  Extended Constituent-to-Dependency Conversion for English , 2007, NODALIDA.

[8]  Richard Johansson,et al.  The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies , 2008, CoNLL.

[9]  Richard Johansson,et al.  The Effect of Syntactic Representation on Semantic Role Labeling , 2008, COLING.

[10]  Daniel Zeman,et al.  Reusable Tagset Conversion Using Tagset Drivers , 2008, LREC.

[11]  Jun'ichi Tsujii,et al.  Task-oriented Evaluation of Syntactic Parsers and Their Representations , 2008, ACL.

[12]  Christopher D. Manning,et al.  The Stanford Typed Dependencies Representation , 2008, CF+CDPE@COLING.

[13]  Sampo Pyysalo,et al.  Overview of BioNLP’09 Shared Task on Event Extraction , 2009, BioNLP@HLT-NAACL.

[14]  Udo Hahn,et al.  Evaluating the Impact of Alternative Dependency Graph Encodings on Solving Event Extraction Tasks , 2010, EMNLP.

[15]  Bernd Bohnet,et al.  Top Accuracy and Fast Dependency Parsing is not a Contradiction , 2010, COLING.

[16]  Deniz Yuret,et al.  SemEval-2010 Task 12: Parser Evaluation Using Textual Entailments , 2010, *SEMEVAL.

[17]  Nathan Green,et al.  Influence of Parser Choice on Dependency-Based MT , 2011, WMT@EMNLP.

[18]  Erik Velldal,et al.  UiO 2: Sequence-labeling Negation Using Dependency Features , 2012, *SEMEVAL.

[19]  Stephan Oepen,et al.  Who Did What to Whom? A Contrastive Study of Syntacto-Semantic Dependencies , 2012, LAW@ACL.

[20]  Slav Petrov,et al.  A Universal Part-of-Speech Tagset , 2011, LREC.

[21]  Stephan Oepen,et al.  Speculation and Negation: Rules, Rankers, and the Role of Syntax , 2012, CL.

[22]  Stephan Oepen,et al.  Document Parsing: Towards Realistic Syntactic Analysis , 2013, IWPT.

[23]  Philipp Koehn,et al.  Abstract Meaning Representation for Sembanking , 2013, LAW@ACL.

[24]  Richard Johansson,et al.  Relational Features in Fine-Grained Opinion Analysis , 2013, CL.

[25]  Sigrid Klerke,et al.  Down-stream effects of tree-to-dependency conversions , 2013, HLT-NAACL.

[26]  Joakim Nivre,et al.  Universal Stanford dependencies: A cross-linguistic typology , 2014, LREC.

[27]  Jari Björne,et al.  Biomedical Event Extraction with Machine Learning , 2014 .

[28]  Emily M. Bender,et al.  Simple Negation Scope Resolution through Deep Parsing: A Semantic Solution to a Semantic Problem , 2014, ACL.

[29]  Rudolf Rosa,et al.  HamleDT 2.0: Thirty Dependency Treebanks Stanfordized , 2014, LREC.

[30]  Slav Petrov,et al.  Globally Normalized Transition-Based Neural Networks , 2016, ACL.

[31]  Bonnie L. Webber,et al.  Neural Networks For Negation Scope Detection , 2016, ACL.

[32]  Stephan Oepen,et al.  Towards a Catalogue of Linguistic Graph Banks , 2016, Computational Linguistics.

[33]  David Vilares,et al.  How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Sentiment Analysis , 2017, ArXiv.

[34]  Benoît Sagot,et al.  Paris and Stanford at EPE 2017: Downstream Evaluation of Graph-based Dependency Representations , 2017 .

[35]  Noah A. Smith,et al.  Deep Multitask Learning for Semantic Dependency Parsing , 2017, ACL.

[36]  Timothy Dozat,et al.  Stanford’s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task , 2017, CoNLL.

[37]  Leo Wanner,et al.  UPF at EPE 2017: transduction-based deep analysis , 2017 .

[38]  Nizar Habash,et al.  CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies , 2017, CoNLL.

[39]  Milan Straka,et al.  Prague at EPE 2017: The UDPipe System , 2017 .