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Found 34 papers, 6 papers with code
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities
Scholars in inter-disciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora.
"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter
Social media users often make explicit predictions about upcoming events.
He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics
We investigate how well BERT performs on predicting factuality in several existing English datasets, encompassing various linguistic constructions.
Investigating Reasons for Disagreement in Natural Language Inference
We investigate how disagreement in natural language inference (NLI) annotation arises.
Ecologically Valid Explanations for Label Variation in NLI
Human label variation, or annotation disagreement, exists in many natural language processing (NLP) tasks, including natural language inference (NLI).
``i have a feeling trump will win..................'': Forecasting Winners and Losers from User Predictions on Twitter
Social media users often make explicit predictions about upcoming events.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
QED: A fact verification system for the FEVER shared task
This paper describes our system submission to the 2018 Fact Extraction and VERification (FEVER) shared task.
Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems
This paper describes our {``}breaker{''} submission to the 2017 EMNLP {``}Build It Break It{''} shared task on sentiment analysis.
Results of the WNUT16 Named Entity Recognition Shared Task
This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating.
Challenges and Solutions for Latin Named Entity Recognition
Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English.
Universal Stanford dependencies: A cross-linguistic typology
Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones.
A Gold Standard Dependency Corpus for English
This resource addresses the lack of a gold standard dependency treebank for English, as well as the limited availability of gold standard syntactic annotations for English informal text genres.
Universal Dependencies v1: A Multilingual Treebank Collection
Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments.
Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment
Here, we explore the hypothesis that linguistic deficits drive the error patterns of existing speaker commitment models by analyzing the linguistic correlates of model error on a challenging naturalistic dataset.
Evaluating BERT for natural language inference: A case study on the CommitmentBank
Natural language inference (NLI) datasets (e. g., MultiNLI) were collected by soliciting hypotheses for a given premise from annotators.
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework.
Identifying inherent disagreement in natural language inference
Natural language inference (NLI) is the task of determining whether a piece of text is entailed, contradicted by or unrelated to another piece of text.
Contextualized Embeddings for Enriching Linguistic Analyses on Politeness
In this work, we introduce a model which leverages the pre-trained BERT model to cluster contextualized representations of a word based on (1) the context in which the word appears and (2) the labels of items the word occurs in.
Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation
Human label variation (Plank 2022), or annotation disagreement, exists in many natural language processing (NLP) tasks.
VariErr NLI: Separating Annotation Error from Human Label Variation
To fill this gap, we introduce a systematic methodology and a new dataset, VariErr (variation versus error), focusing on the NLI task in English.
