Mining Possessions: Existence, Type and Temporal Anchors
This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between possessor and possessee. We present new annotations for this task, and experimental results using both traditional classifiers and neural networks. Results show that the three subtasks (predicting possession existence, possession type and temporal anchors) can be automated.
Code (0)
등록된 구현이 없습니다.
Tasks
Vocal Bursts Type PredictionSimilar Papers 제목 키워드 기반
Beyond Possession Existence: Duration and Co-Possession
This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of c…
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts
This paper presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. We annotate Wikipedia articles for 90 different well-k…
ArticlesMachine Reading ComprehensionReading ComprehensionRelation+1Building a Dataset for Possessions Identification in Text
Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one{'}s most intimately held thoughts, opinions and belie…
Coach2vec: autoencoding the playing style of soccer coaches
Capturing the playing style of professional soccer coaches is a complex, and yet barely explored, task in sports analytics. Nowadays, the availability of digital data describing every relevant spatio-temporal aspect of s…
Sports AnalyticsThe path to a goal: Understanding soccer possessions via path signatures
We present a novel framework for predicting next actions in soccer possessions by leveraging path signatures to encode their complex spatio-temporal structure. Unlike existing approaches, we do not rely on fixed historic…
Feature Engineering