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Integrating multiple document features in language models for expert finding
We argue that expert finding is sensitive to multiple document features in an organizational intranet. These document features include multiple levels of associations between experts and a query topic from sentence, paragraph, up to document levels, document authority information such as the PageRank, indegree, and URL length of documents, and internal document structures that indicate the experts' relationship with the content of documents. Our assumption is that expert finding can largely benefit from the incorporation of these document features. However, existing language modeling approaches for expert finding have not sufficiently taken into account these document features. We propose a novel language modeling approach, which integrates multiple document features, for expert finding. Our experiments on two large scale TREC Enterprise Track datasets, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios. Our main contribution is to develop an effective formal method for modeling multiple document features in expert finding, and conduct a systematic investigation of their effects. It is worth noting that our novel approach achieves better results in terms of MAP than previous language model based approaches and the best automatic runs in both the TREC2006 and TREC2007 expert search tasks, respectively
Adaptation to Easy Data in Prediction with Limited Advice
We derive an online learning algorithm with improved regret guarantees for
`easy' loss sequences. We consider two types of `easiness': (a) stochastic loss
sequences and (b) adversarial loss sequences with small effective range of the
losses. While a number of algorithms have been proposed for exploiting small
effective range in the full information setting, Gerchinovitz and Lattimore
[2016] have shown the impossibility of regret scaling with the effective range
of the losses in the bandit setting. We show that just one additional
observation per round is sufficient to circumvent the impossibility result. The
proposed Second Order Difference Adjustments (SODA) algorithm requires no prior
knowledge of the effective range of the losses, , and achieves an
expected regret guarantee, where is the time horizon and is the number
of actions. The scaling with the effective loss range is achieved under
significantly weaker assumptions than those made by Cesa-Bianchi and Shamir
[2018] in an earlier attempt to circumvent the impossibility result. We also
provide a regret lower bound of , which almost
matches the upper bound. In addition, we show that in the stochastic setting
SODA achieves an pseudo-regret bound that holds simultaneously
with the adversarial regret guarantee. In other words, SODA is safe against an
unrestricted oblivious adversary and provides improved regret guarantees for at
least two different types of `easiness' simultaneously.Comment: Fixed a mistake in the proof and statement of Theorem
Extracting Temporal Expressions from Unstructured Open Resources
AETAS is an end-to-end system with SOA approach that retrieves plain text data from web and blog news and represents and stores them in RDF, with a special focus on their temporal dimension. The system allows users to acquire, browse and query Linked Data obtained from unstructured sources
PVT3D: Point Voxel Transformers for Place Recognition from Sparse Lidar Scans
Place recognition based on point cloud (LiDAR) scans is an important module
for achieving robust autonomy in robots or self-driving vehicles. Training deep
networks to match such scans presents a difficult trade-off: a higher spatial
resolution of the network's intermediate representations is needed to perform
fine-grained matching of subtle geometric features, but growing it too large
makes the memory requirements infeasible. In this work, we propose a
Point-Voxel Transformer network (PVT3D) that achieves robust fine-grained
matching with low memory requirements. It leverages a sparse voxel branch to
extract and aggregate information at a lower resolution and a point-wise branch
to obtain fine-grained local information. A novel hierarchical cross-attention
transformer (HCAT) uses queries from one branch to try to match structures in
the other branch, ensuring that both extract self-contained descriptors of the
point cloud (rather than one branch dominating), but using both to inform the
output global descriptor of the point cloud. Extensive experiments show that
the proposed PVT3D method surpasses the state-of-the-art by a large amount on
several datasets (Oxford RobotCar, TUM, USyd). For instance, we achieve AR@1 of
85.6% on the TUM dataset, which surpasses the strongest prior model by ~15%.Comment: 11 pages, 7 figures, 5 table
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