Annotating If the Authors of a Tweet are Located at the Locations They Tweet About (L18-1)
Copied to clipboard
| Challenge: | a tweet's locations do not always indicate spatial information involving the author of the tweet . a corpus of 1,062 tweets contains 1,200 location named entities . |
| Approach: | They propose a corpus annotating whether tweet authors are located in locations . they use temporal tags centered around tweet timestamps to temporally anchor this information . |
| Outcome: | The proposed method annotates whether authors are located in tweet locations . it shows that no spatial relationship can be inferred in 21% of instances . |
Similar Papers
Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach (2022.coling-1)
Copied to clipboard
| Challenge: | Experimental results show that a neural architecture that combines both modalities yields better results. |
| Approach: | They propose a neural architecture that combines both modalities to solve the problem of determining whether people are located in tweets. |
| Outcome: | The proposed model combines both modalities to produce better results . |
Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies (L18-1)
Copied to clipboard
| Challenge: | Existing approaches to extract spatial knowledge focus on extracting locations of events, someone or something. |
| Approach: | They propose a method to annotate temporally-anchored spatial knowledge on top of OntoNotes by crowdsourcing annotations. |
| Outcome: | The proposed method can be automated and validated using syntactic dependencies and crowdsourced annotations. |
Identification of Fine-Grained Location Mentions in Crisis Tweets (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent studies have focused on identifying informative tweets by individuals affected by a crisis, without considering their specific types. |
| Approach: | They assemble two tweet crisis datasets and manually annotate them with specific location types to facilitate progress on the fine-grained location identification task. |
| Outcome: | The proposed model performs well in both in-domain and cross-domain settings. |
Geo-Seq2seq: Twitter User Geolocation on Noisy Data through Sequence to Sequence Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | a new method for Twitter user geolocation rewrites noisy, multilingual location strings into structured English location names. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) model that rewrites noisy location strings into structured English location names. |
| Outcome: | The proposed model can generalize well to unseen temporal data, but performance does vary by language and country. |
Tagging Location Phrases in Text (2020.lrec-1)
Copied to clipboard
| Challenge: | a number of studies have focused on detecting named entities in written language. |
| Approach: | They describe a Location Phrase Detection task to detect non-named locations . they use sequential tagging and an annotation approach to create annotated datasets . |
| Outcome: | The proposed task can detect non-named locations in English and Russian news . the authors develop a sequential tagging approach and annotate datasets for English and Russia . |
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
Copied to clipboard
| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
| Outcome: | The proposed models achieve state-of-the-art on two datasets labelled manually and via tag-based distant supervision indicating a difference between intended and perceived sarcasm . |
Annotations for Exploring Food Tweets from Multiple Aspects (2024.lrec-main)
Copied to clipboard
| Challenge: | The Latvian Twitter Eater Corpus (LTEC) is a collection of tweets gathered by following the appearance of 363 keywords related to food, drinks, eating and drinking in various valid word forms in the Latvian language. |
| Approach: | They build upon the Latvian Twitter Eater Corpus which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. |
| Outcome: | The Latvian Twitter Eater Corpus (LTEC) is a collection of tweets gathered by following the appearance of 363 keywords related to food and eating inflected in various valid word forms in the Latvian language. |
Temporally-Informed Analysis of Named Entity Recognition (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods to evaluate text data are rarely reported by taking the timestamp of the document into account. |
| Approach: | They propose methods that make better use of temporally-diverse training data with a focus on named entity recognition. |
| Outcome: | The proposed models make better use of temporally-diverse training data, with a focus on named entity recognition. |
NarrativeTime: Dense Temporal Annotation on a Timeline (2024.lrec-main)
Copied to clipboard
| Challenge: | e.g. TimeBank contains 1-5% of all possible tlinks, and this information is underspecified in the text. |
| Approach: | They propose a timeline-based framework that achieves full coverage of all possible TLINKs. |
| Outcome: | The proposed framework achieves full coverage of all possible TLINKs in a text. |
Who Wrote When? Author Diarization in Social Media Discussions (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for author diarization are unable to detect stylistic shifts in a text . |
| Approach: | They propose a framework that integrates pre-trained neural representations of writing style with author-conditional encoder-decoder diarization. |
| Outcome: | The proposed framework is able to attribute comments in online discussions to individual authors. |