659 research outputs found
"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.
Such statements vary in the degree of certainty the author expresses toward the
outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win"
or "No way Leonardo wins!". Can popular beliefs on social media predict who
will win? To answer this question, we build a corpus of tweets annotated for
veridicality on which we train a log-linear classifier that detects positive
veridicality with high precision. We then forecast uncertain outcomes using the
wisdom of crowds, by aggregating users' explicit predictions. Our method for
forecasting winners is fully automated, relying only on a set of contenders as
input. It requires no training data of past outcomes and outperforms sentiment
and tweet volume baselines on a broad range of contest prediction tasks. We
further demonstrate how our approach can be used to measure the reliability of
individual accounts' predictions and retrospectively identify surprise
outcomes.Comment: Accepted at EMNLP 2017 (long paper
Investigating Reasons for Disagreement in Natural Language Inference
We investigate how disagreement in natural language inference (NLI)
annotation arises. We developed a taxonomy of disagreement sources with 10
categories spanning 3 high-level classes. We found that some disagreements are
due to uncertainty in the sentence meaning, others to annotator biases and task
artifacts, leading to different interpretations of the label distribution. We
explore two modeling approaches for detecting items with potential
disagreement: a 4-way classification with a "Complicated" label in addition to
the three standard NLI labels, and a multilabel classification approach. We
found that the multilabel classification is more expressive and gives better
recall of the possible interpretations in the data.Comment: accepted at TACL, pre-MIT Press publication versio
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. Data sparsity in Latin presents a number of challenges for traditional Named Entity
Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track
the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90% F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree
of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality
The prosody of presupposition projection in naturally-occurring utterances
In experimental studies, prosodically-marked pragmatic focus has been found to influence the projection of factive presuppositions of utterances like these parents didnât know the kid was gone (Cummins and Rohde, 2015; Tonhauser, 2016; Djšarv and Bacovcin, 2017), supporting question-based analyses of projection (i.a., AbrusÂŽan, 2011; AbrusÂŽan, 2016; Simons et al., 2017; Beaver et al., 2017). However, no prior work has explored whether this effect extends to naturally-occurring utterances. In a large set of naturally-occurring utterances, we find that prosodically-marked focus influences projection in utterances with factive embedding predicates, but not those with non-factive predicates. We argue that our findings support an account where lexical semantics of the predicate contributes to projection to the extent that they admit QUD alternatives that can be assumed to entail the content of the complement
Deviations from plastic barriers in BiSrCaCuO thin films
Resistive transitions of an epitaxial BiSrCaCuO thin
film were measured in various magnetic fields (), ranging from 0
to 22.0 T. Rounded curvatures of low resistivity tails are observed in
Arrhenius plot and considered to relate to deviations from plastic barriers. In
order to characterize these deviations, an empirical barrier form is developed,
which is found to be in good agreement with experimental data and coincide with
the plastic barrier form in a limited magnetic field range. Using the plastic
barrier predictions and the empirical barrier form, we successfully explain the
observed deviations.Comment: 5 pages, 6 figures; PRB 71, 052502 (2005
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). To
gain direct evidence of how NLI label variation arises, we build LiveNLI, an
English dataset of 1,415 ecologically valid explanations (annotators explain
the NLI labels they chose) for 122 MNLI items (at least 10 explanations per
item). The LiveNLI explanations confirm that people can systematically vary on
their interpretation and highlight within-label variation: annotators sometimes
choose the same label for different reasons. This suggests that explanations
are crucial for navigating label interpretations in general. We few-shot prompt
large language models to generate explanations but the results are
inconsistent: they sometimes produces valid and informative explanations, but
it also generates implausible ones that do not support the label, highlighting
directions for improvement.Comment: Findings at EMNLP 2023. Overlap with previous version
arXiv:2304.1244
Over âsexedâ regulation and the disregarded worker: an overview of the impact of sexual entertainment policy on lap-dancing club workers
In England and Wales, with the introduction of Section 27 of the Policing and Crime Act 2009, lap-dancing clubs can now be licensed as Sexual Entertainment Venues. This article considers such, offering a critique of Section 27, arguing that this legislation is not evidence-based, with lap-dancing policy, like other sex-work policies, often associated with crime, deviance and immorality. Furthermore, it is argued that sex-work policies are gradually being homogenised as well as increasingly criminalised. Other criticisms relate to various licensing loopholes which lead to some striptease venues remaining unlicensed and unregulated, potentially impacting on the welfare of erotic dancers. In addition, restrictions on the numbers of lap-dancing venues may exacerbate dancer unemployment, drawing these women into poverty. Finally, The Policing and Crime Act reflects how the political focus is being directed away from the exploitation of workers, on to issues relating
to crime and deviance, despite limited evidence to support this focus
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