7 research outputs found
Predicting Semantic Relations using Global Graph Properties
Semantic graphs, such as WordNet, are resources which curate natural language
on two distinguishable layers. On the local level, individual relations between
synsets (semantic building blocks) such as hypernymy and meronymy enhance our
understanding of the words used to express their meanings. Globally, analysis
of graph-theoretic properties of the entire net sheds light on the structure of
human language as a whole. In this paper, we combine global and local
properties of semantic graphs through the framework of Max-Margin Markov Graph
Models (M3GM), a novel extension of Exponential Random Graph Model (ERGM) that
scales to large multi-relational graphs. We demonstrate how such global
modeling improves performance on the local task of predicting semantic
relations between synsets, yielding new state-of-the-art results on the WN18RR
dataset, a challenging version of WordNet link prediction in which "easy"
reciprocal cases are removed. In addition, the M3GM model identifies
multirelational motifs that are characteristic of well-formed lexical semantic
ontologies.Comment: EMNLP 201
Technological taxonomies for hypernym and hyponym retrieval in patent texts
This paper presents an automatic approach to creating taxonomies of technical
terms based on the Cooperative Patent Classification (CPC). The resulting
taxonomy contains about 170k nodes in 9 separate technological branches and is
freely available. We also show that a Text-to-Text Transfer Transformer (T5)
model can be fine-tuned to generate hypernyms and hyponyms with relatively high
precision, confirming the manually assessed quality of the resource. The T5
model opens the taxonomy to any new technological terms for which a hypernym
can be generated, thus making the resource updateable with new terms, an
essential feature for the constantly evolving field of technological
terminology.Comment: ToTh 2022 - Terminology & Ontology: Theories and applications, Jun
2022, Chamb{\'e}ry, Franc
Improving Neural Relation Extraction with Implicit Mutual Relations
Relation extraction (RE) aims at extracting the relation between two entities
from the text corpora. It is a crucial task for Knowledge Graph (KG)
construction. Most existing methods predict the relation between an entity pair
by learning the relation from the training sentences, which contain the
targeted entity pair. In contrast to existing distant supervision approaches
that suffer from insufficient training corpora to extract relations, our
proposal of mining implicit mutual relation from the massive unlabeled corpora
transfers the semantic information of entity pairs into the RE model, which is
more expressive and semantically plausible. After constructing an entity
proximity graph based on the implicit mutual relations, we preserve the
semantic relations of entity pairs via embedding each vertex of the graph into
a low-dimensional space. As a result, we can easily and flexibly integrate the
implicit mutual relations and other entity information, such as entity types,
into the existing RE methods.
Our experimental results on a New York Times and another Google Distant
Supervision datasets suggest that our proposed neural RE framework provides a
promising improvement for the RE task, and significantly outperforms the
state-of-the-art methods. Moreover, the component for mining implicit mutual
relations is so flexible that can help to improve the performance of both
CNN-based and RNN-based RE models significant.Comment: 12 page
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by
extracting entity relations from texts.However, it usually suffers from the
long-tail issue. The training data mainly concentrates on a few types of
relations, leading to the lackof sufficient annotations for the remaining types
of relations. In this paper, we propose a general approach to learn relation
prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction
by transferring knowledge from the relation types with sufficient trainingdata.
We learn relation prototypes as an implicit factor between entities, which
reflects the meanings of relations as well as theirproximities for transfer
learning. Specifically, we construct a co-occurrence graph from texts, and
capture both first-order andsecond-order entity proximities for embedding
learning. Based on this, we further optimize the distance from entity pairs
tocorresponding prototypes, which can be easily adapted to almost arbitrary RE
frameworks. Thus, the learning of infrequent or evenunseen relation types will
benefit from semantically proximate relations through pairs of entities and
large-scale textual information.We have conducted extensive experiments on two
publicly available datasets: New York Times and Google Distant
Supervision.Compared with eight state-of-the-art baselines, our proposed model
achieves significant improvements (4.1% F1 on average). Furtherresults on
long-tail relations demonstrate the effectiveness of the learned relation
prototypes. We further conduct an ablation study toinvestigate the impacts of
varying components, and apply it to four basic relation extraction models to
verify the generalization ability.Finally, we analyze several example cases to
give intuitive impressions as qualitative analysis. Our codes will be released
later