Search Results for author:
Found 38 papers, 8 papers with code
Who’s in, who’s out? Predicting the Inclusiveness or Exclusiveness of Personal Pronouns in Parliamentary Debates
This paper presents a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, that is their ability to construct and manage in-groups and out-groups by including/excluding the audience and/or non-speech act participants in reference to groups that also include the speaker.
I’ve got a construction looks funny – representing and recovering non-standard constructions in UD
We argue that a unified treatment of constructions across languages will increase the consistency of the UD annotations and thus the quality of the treebanks for linguistic analysis.
Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach
We address the task of distinguishing implicitly abusive sentences on identity groups (“Muslims contaminate our planet”) from other group-related negative polar sentences (“Muslims despise terrorism”).
Who is we? Disambiguating the referents of first person plural pronouns in parliamentary debates
This paper investigates the use of first person plural pronouns as a rhetorical device in political speeches.
Implicitly Abusive Language -- What does it actually look like and why are we not getting there?
Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently.
Implicitly Abusive Comparisons -- A New Dataset and Linguistic Analysis
We examine the task of detecting implicitly abusive comparisons (e. g. {``}Your hair looks like you have been electrocuted{''}).
Exploiting Emojis for Abusive Language Detection
We propose to use abusive emojis, such as the {``}middle finger{''} or {``}face vomiting{''}, as a proxy for learning a lexicon of abusive words.
Treebanking User-Generated Content: a UD Based Overview of Guidelines, Corpora and Unified Recommendations
This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis.
A New Resource for German Causal Language
In the paper, we present inter-annotator agreement scores for our dataset and discuss problems for annotating causal language.
Fine-grained Named Entity Annotations for German Biographic Interviews
Building on the OntoNotes 5. 0 NER inventory, our scheme is adapted for a corpus of transcripts of biographic interviews by adding categories for AGE and LAN(guage) and also features extended numeric and temporal categories.
Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies
The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework.
Improving Sentence Boundary Detection for Spoken Language Transcripts
For the detection of boundaries in spoken language transcripts, we achieve a substantial improvement when framing the boundary detection problem assentence pair classification task, as compared to a sequence tagging approach.
Sprucing up the trees -- Error detection in treebanks
We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning.
Detecting annotation noise in automatically labelled data
We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost.
Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants
In this paper, we present a simple, yet effective method for the automatic identification and extraction of causal relations from text, based on a large English-German parallel corpus.
Effect Functors for Opinion Inference
We also present results of a crowdsourcing experiment to test the utility of some known and some new functors for opinion inference where, unlike in previous work, subjects are asked to reason from event evaluation to participant evaluation.
MLSA --- A Multi-layered Reference Corpus for German Sentiment Analysis
The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity.
Yes we can!? Annotating English modal verbs
This paper presents an annotation scheme for English modal verbs together with sense-annotated data from the news domain.
