Search Results for author: Juntao Yu

Found 23 papers, 10 papers with code

The CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

no code implementations COLING (CODI, CRAC) 2022 Juntao Yu, Sopan Khosla, Ramesh Manuvinakurike, Lori Levin, Vincent Ng, Massimo Poesio, Michael Strube, Carolyn Rosé

The CODI-CRAC 2022 Shared Task on Anaphora Resolution in Dialogues is the second edition of an initiative focused on detecting different types of anaphoric relations in conversations of different kinds.

The Universal Anaphora Scorer

no code implementations LREC 2022 Juntao Yu, Sopan Khosla, Nafise Sadat Moosavi, Silviu Paun, Sameer Pradhan, Massimo Poesio

It also supports the evaluation of split antecedent anaphora and discourse deixis, for which no tools existed.

Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts

no code implementations11 Oct 2022 Juntao Yu, Silviu Paun, Maris Camilleri, Paloma Carretero Garcia, Jon Chamberlain, Udo Kruschwitz, Massimo Poesio

Although several datasets annotated for anaphoric reference/coreference exist, even the largest such datasets have limitations in terms of size, range of domains, coverage of anaphoric phenomena, and size of documents included.

2k

Scoring Coreference Chains with Split-Antecedent Anaphors

1 code implementation24 May 2022 Silviu Paun, Juntao Yu, Nafise Sadat Moosavi, Massimo Poesio

Anaphoric reference is an aspect of language interpretation covering a variety of types of interpretation beyond the simple case of identity reference to entities introduced via nominal expressions covered by the traditional coreference task in its most recent incarnation in ONTONOTES and similar datasets.

Stay Together: A System for Single and Split-antecedent Anaphora Resolution

1 code implementation NAACL 2021 Juntao Yu, Nafise Sadat Moosavi, Silviu Paun, Massimo Poesio

Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available.

Neural Coreference Resolution for Arabic

1 code implementation COLING (CRAC) 2020 Abdulrahman Aloraini, Juntao Yu, Massimo Poesio

No neural coreference resolver for Arabic exists, in fact we are not aware of any learning-based coreference resolver for Arabic since (Bjorkelund and Kuhn, 2014).

coreference-resolution

Named Entity Recognition as Dependency Parsing

1 code implementation ACL 2020 Juntao Yu, Bernd Bohnet, Massimo Poesio

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities.

Dependency Parsing named-entity-recognition +4

A Cluster Ranking Model for Full Anaphora Resolution

1 code implementation LREC 2020 Juntao Yu, Alexandra Uma, Massimo Poesio

In this paper, we introduce an architecture to simultaneously identify non-referring expressions (including expletives, predicative s, and other types) and build coreference chains, including singletons.

Coreference Resolution

A Mention-Pair Model of Annotation with Nonparametric User Communities

no code implementations25 Sep 2019 Silviu Paun, Juntao Yu, Jon Chamberlain, Udo Kruschwitz, Massimo Poesio

The model is also flexible enough to be used in standard annotation tasks for classification where it registers on par performance with the state of the art.

Neural Mention Detection

1 code implementation LREC 2020 Juntao Yu, Bernd Bohnet, Massimo Poesio

We then evaluate our models for coreference resolution by using mentions predicted by our best model in start-of-the-art coreference systems.

coreference-resolution NER

Crowdsourcing and Aggregating Nested Markable Annotations

1 code implementation ACL 2019 Chris Madge, Juntao Yu, Jon Chamberlain, Udo Kruschwitz, Silviu Paun, Massimo Poesio

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation.

coreference-resolution Entity Resolution +1

A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation

no code implementations NAACL 2019 Massimo Poesio, Jon Chamberlain, Silviu Paun, Juntao Yu, Alex Uma, ra, Udo Kruschwitz

The corpus, containing annotations for about 108, 000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2. 2M in total.

Semi-Supervised Methods for Out-of-Domain Dependency Parsing

no code implementations4 Oct 2018 Juntao Yu

Dependency parsing is one of the important natural language processing tasks that assigns syntactic trees to texts.

Dependency Parsing

Anaphora Resolution with the ARRAU Corpus

no code implementations WS 2018 Massimo Poesio, Yulia Grishina, Varada Kolhatkar, Nafise Moosavi, Ina Roesiger, Adam Roussel, Fabian Simonjetz, Alex Uma, ra, Olga Uryupina, Juntao Yu, Heike Zinsmeister

The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference).

Dependency Language Models for Transition-based Dependency Parsing

no code implementations WS 2017 Juntao Yu, Bernd Bohnet

In this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus.

Transition-Based Dependency Parsing

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