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Inducing relational knowledge from BERT
One of the most remarkable properties of word embeddings is the fact that they capture
certain types of semantic and syntactic relationships. Recently, pre-trained language models …
certain types of semantic and syntactic relationships. Recently, pre-trained language models …
On the systematicity of probing contextualized word representations: The case of hypernymy in BERT
Contextualized word representations have become a driving force in NLP, motivating
widespread interest in understanding their capabilities and the mechanisms by which they …
widespread interest in understanding their capabilities and the mechanisms by which they …
[کتاب][B] Distributional semantics
A Lenci, M Sahlgren - 2023 - books.google.com
Distributional semantics develops theories and methods to represent the meaning of natural
language expressions, with vectors encoding their statistical distribution in linguistic …
language expressions, with vectors encoding their statistical distribution in linguistic …
Relational word embeddings
While word embeddings have been shown to implicitly encode various forms of attributional
knowledge, the extent to which they capture relational information is far more limited. In …
knowledge, the extent to which they capture relational information is far more limited. In …
RelBERT: Embedding Relations with Language Models
Many applications need access to background knowledge about how different concepts and
entities are related. Although Knowledge Graphs (KG) and Large Language Models (LLM) …
entities are related. Although Knowledge Graphs (KG) and Large Language Models (LLM) …
Combining vision and language representations for patch-based identification of Lexico-semantic relations
Although a wide range of applications have been proposed in the field of multimodal natural
language processing, very few works have been tackling multimodal relational lexical …
language processing, very few works have been tackling multimodal relational lexical …
[PDF][PDF] A latent variable model for learning distributional relation vectors
J Camacho Collados, L Espinosa-Anke, S Jameel… - 2019 - orca.cardiff.ac.uk
Recently a number of unsupervised approaches have been proposed for learning vectors
that capture the relationship between two words. Inspired by word embedding models, these …
that capture the relationship between two words. Inspired by word embedding models, these …
Tifi: Taxonomy induction for fictional domains
Taxonomies are important building blocks of structured knowledge bases, and their
construction from text sources and Wikipedia has received much attention. In this paper we …
construction from text sources and Wikipedia has received much attention. In this paper we …
Minimally-supervised relation induction from pre-trained language model
Relation Induction is a very practical task in Natural Language Processing (NLP) area. In
practical application scenarios, people want to induce more entity pairs having the same …
practical application scenarios, people want to induce more entity pairs having the same …
[PDF][PDF] Understanding feature focus in multitask settings for lexico-semantic relation identification
Discovering whether words are semantically related and identifying the specific semantic
relation that holds between them is of crucial importance for automatic reasoning on text …
relation that holds between them is of crucial importance for automatic reasoning on text …