3 research outputs found
Entity Linking in 40 Languages using MAG
A plethora of Entity Linking (EL) approaches has recently been developed.
While many claim to be multilingual, the MAG (Multilingual AGDISTIS) approach
has been shown recently to outperform the state of the art in multilingual EL
on 7 languages. With this demo, we extend MAG to support EL in 40 different
languages, including especially low-resources languages such as Ukrainian,
Greek, Hungarian, Croatian, Portuguese, Japanese and Korean. Our demo relies on
online web services which allow for an easy access to our entity linking
approaches and can disambiguate against DBpedia and Wikidata. During the demo,
we will show how to use MAG by means of POST requests as well as using its
user-friendly web interface. All data used in the demo is available at
https://hobbitdata.informatik.uni-leipzig.de/agdistis/Comment: Accepted at ESWC 201
Template-based Question Answering using Recursive Neural Networks
We propose a neural network-based approach to automatically learn and
classify natural language questions into its corresponding template using
recursive neural networks. An obvious advantage of using neural networks is the
elimination of the need for laborious feature engineering that can be
cumbersome and error-prone. The input question is encoded into a vector
representation. The model is trained and evaluated on the LC-QuAD dataset
(Large-scale Complex Question Answering Dataset). The LC-QuAD queries are
annotated based on 38 unique templates that the model attempts to classify. The
resulting model is evaluated against both the LC-QuAD dataset and the 7th
Question Answering Over Linked Data (QALD-7) dataset. The recursive neural
network achieves template classification accuracy of 0.828 on the LC-QuAD
dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most
likely templates were considered the model achieves an accuracy of 0.945 on the
LC-QuAD dataset and 0.786 on the QALD-7 dataset. After slot filling, the
overall system achieves a macro F-score 0.419 on the LC-QuAD dataset and a
macro F-score of 0.417 on the QALD-7 dataset
Knowledge Graphs for Multilingual Language Translation and Generation
The Natural Language Processing (NLP) community has recently seen outstanding
progress, catalysed by the release of different Neural Network (NN)
architectures. Neural-based approaches have proven effective by significantly
increasing the output quality of a large number of automated solutions for NLP
tasks (Belinkov and Glass, 2019). Despite these notable advancements, dealing
with entities still poses a difficult challenge as they are rarely seen in
training data. Entities can be classified into two groups, i.e., proper nouns
and common nouns. Proper nouns are also known as Named Entities (NE) and
correspond to the name of people, organizations, or locations, e.g., John, WHO,
or Canada. Common nouns describe classes of objects, e.g., spoon or cancer.
Both types of entities can be found in a Knowledge Graph (KG). Recent work has
successfully exploited the contribution of KGs in NLP tasks, such as Natural
Language Inference (NLI) (KM et al.,2018) and Question Answering (QA) (Sorokin
and Gurevych, 2018). Only a few works had exploited the benefits of KGs in
Neural Machine Translation (NMT) when the work presented herein began.
Additionally, few works had studied the contribution of KGs to Natural Language
Generation (NLG) tasks. Moreover, the multilinguality also remained an open
research area in these respective tasks (Young et al., 2018). In this thesis,
we focus on the use of KGs for machine translation and the generation of texts
to deal with the problems caused by entities and consequently enhance the
quality of automatically generated texts