251 research outputs found
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
Modeling hypernymy, such as poodle is-a dog, is an important generalization
aid to many NLP tasks, such as entailment, coreference, relation extraction,
and question answering. Supervised learning from labeled hypernym sources, such
as WordNet, limits the coverage of these models, which can be addressed by
learning hypernyms from unlabeled text. Existing unsupervised methods either do
not scale to large vocabularies or yield unacceptably poor accuracy. This paper
introduces distributional inclusion vector embedding (DIVE), a
simple-to-implement unsupervised method of hypernym discovery via per-word
non-negative vector embeddings which preserve the inclusion property of word
contexts in a low-dimensional and interpretable space. In experimental
evaluations more comprehensive than any previous literature of which we are
aware-evaluating on 11 datasets using multiple existing as well as newly
proposed scoring functions-we find that our method provides up to double the
precision of previous unsupervised embeddings, and the highest average
performance, using a much more compact word representation, and yielding many
new state-of-the-art results.Comment: NAACL 201
Formulating the Bonding Contribution Equation in Heterogeneous Catalysis:A Quantitative Description between Surface Structure and Adsorption Energy
The relation between the surface structure and adsorption energy of adsorbates is of great importance in heterogeneous catalysis.</p
EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual Hulls
3D reconstruction from multiple views is a successful computer vision field
with multiple deployments in applications. State of the art is based on
traditional RGB frames that enable optimization of photo-consistency cross
views. In this paper, we study the problem of 3D reconstruction from
event-cameras, motivated by the advantages of event-based cameras in terms of
low power and latency as well as by the biological evidence that eyes in nature
capture the same data and still perceive well 3D shape. The foundation of our
hypothesis that 3D reconstruction is feasible using events lies in the
information contained in the occluding contours and in the continuous scene
acquisition with events. We propose Apparent Contour Events (ACE), a novel
event-based representation that defines the geometry of the apparent contour of
an object. We represent ACE by a spatially and temporally continuous implicit
function defined in the event x-y-t space. Furthermore, we design a novel
continuous Voxel Carving algorithm enabled by the high temporal resolution of
the Apparent Contour Events. To evaluate the performance of the method, we
collect MOEC-3D, a 3D event dataset of a set of common real-world objects. We
demonstrate the ability of EvAC3D to reconstruct high-fidelity mesh surfaces
from real event sequences while allowing the refinement of the 3D
reconstruction for each individual event.Comment: 16 pages, 8 figures, European Conference on Computer Vision (ECCV)
202
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of
70, 000 sentences on 100 relations derived from Wikipedia and annotated by
crowdworkers. The relation of each sentence is first recognized by distant
supervision methods, and then filtered by crowdworkers. We adapt the most
recent state-of-the-art few-shot learning methods for relation classification
and conduct a thorough evaluation of these methods. Empirical results show that
even the most competitive few-shot learning models struggle on this task,
especially as compared with humans. We also show that a range of different
reasoning skills are needed to solve our task. These results indicate that
few-shot relation classification remains an open problem and still requires
further research. Our detailed analysis points multiple directions for future
research. All details and resources about the dataset and baselines are
released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is
determined by dice rolling. Visit our website http://zhuhao.me/fewre
- …