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Found 15 papers, 6 papers with code
Entity Linking in the ParlaMint Corpus
In this paper, we investigate the task of linking entities from ParlaMint in different languages to a knowledge base, and evaluating the performance of three entity linking methods.
The Role of Complex NLP in Transformers for Text Ranking?
Even though term-based methods such as BM25 provide strong baselines in ranking, under certain conditions they are dominated by large pre-trained masked language models (MLMs) such as BERT.
How Different are Pre-trained Transformers for Text Ranking?
Our results contribute to our understanding of (black-box) neural rankers relative to (well-understood) traditional rankers, help understand the particular experimental setting of MS-Marco-based test collections.
Who mentions whom? Recognizing political actors in proceedings
We show that it is straightforward to train a state of the art named entity tagger (spaCy) to recognize political actors in Dutch parliamentary proceedings with high accuracy.
Learning from Samples of Variable Quality
Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing.
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of Documents
For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.
Learning to Rank from Samples of Variable Quality
To this end, we introduce "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.
Learning to Learn from Weak Supervision by Full Supervision
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels.
Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
We propose an approach for detecting semantic shifts between different viewpoints--broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party.
Fidelity-Weighted Learning
To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Deep neural networks have become a primary tool for solving problems in many fields.
Neural Ranking Models with Weak Supervision
Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections.
Ranked #8 on
Ad-Hoc Information Retrieval
on TREC Robust04
(MAP metric)
On Horizontal and Vertical Separation in Hierarchical Text Classification
Extracting separable models of hierarchical entities requires us to take their relative position into account and to consider the different types of dependencies in the hierarchy.
A Hybrid Approach to Domain-Specific Entity Linking
The current state-of-the-art Entity Linking (EL) systems are geared towards corpora that are as heterogeneous as the Web, and therefore perform sub-optimally on domain-specific corpora.
