119,466 research outputs found
Joint Inference for Knowledge Base Population
Populating Knowledge Base (KB) with new knowledge facts from reliable text resources usually consists of linking name mentions to KB entities and identifying relationship between entity pairs. However, the task often suffers from errors propagating from upstream entity linkers to downstream relation extractors. In this paper, we propose a novel joint inference framework to allow interactions between the two subtasks and find an optimal assignment by addressing the coherence among preliminary local predictions: whether the types of entities meet the expectations of relations explicitly or implicitly, and whether the local predictions are globally compatible. We further measure the confidence of the extracted triples by looking at the details of the complete extraction process. Experiments show that the proposed framework can significantly reduce the error propagations thus obtain more reliable facts, and outperforms competitive baselines with state-of-the-art relation extraction models. ? 2014 Association for Computational Linguistics.EI
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Social media provide access to behavioural data at an unprecedented scale and
granularity. However, using these data to understand phenomena in a broader
population is difficult due to their non-representativeness and the bias of
statistical inference tools towards dominant languages and groups. While
demographic attribute inference could be used to mitigate such bias, current
techniques are almost entirely monolingual and fail to work in a global
environment. We address these challenges by combining multilingual demographic
inference with post-stratification to create a more representative population
sample. To learn demographic attributes, we create a new multimodal deep neural
architecture for joint classification of age, gender, and organization-status
of social media users that operates in 32 languages. This method substantially
outperforms current state of the art while also reducing algorithmic bias. To
correct for sampling biases, we propose fully interpretable multilevel
regression methods that estimate inclusion probabilities from inferred joint
population counts and ground-truth population counts. In a large experiment
over multilingual heterogeneous European regions, we show that our demographic
inference and bias correction together allow for more accurate estimates of
populations and make a significant step towards representative social sensing
in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web
Conference (WWW '19
- …