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Evolution of semantic similarity—a survey
Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …
research problems in the field of Natural Language Processing (NLP). The versatility of …
WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
By design, word embeddings are unable to model the dynamic nature of words' semantics,
ie, the property of words to correspond to potentially different meanings. To address this …
ie, the property of words to correspond to potentially different meanings. To address this …
[کتاب][B] Exploiting linked data and knowledge graphs in large organisations
JZ Pan, G Vetere, JM Gomez-Perez, H Wu - 2017 - Springer
A few years after Google announced that their 'Knowledge Graph'would have allowed
searching for things, not strings, 1 knowledge graphs start entering information retrieval …
searching for things, not strings, 1 knowledge graphs start entering information retrieval …
Analysis and evaluation of language models for word sense disambiguation
Transformer-based language models have taken many fields in NLP by storm. BERT and its
derivatives dominate most of the existing evaluation benchmarks, including those for Word …
derivatives dominate most of the existing evaluation benchmarks, including those for Word …
[PDF][PDF] Automatic feature engineering for answer selection and extraction
This paper proposes a framework for automatically engineering features for two important
tasks of question answering: answer sentence selection and answer extraction. We …
tasks of question answering: answer sentence selection and answer extraction. We …
Predicting the semantic textual similarity with siamese CNN and LSTM
Semantic Textual Similarity (STS) is the basis of many applications in Natural Language
Processing (NLP). Our system combines convolution and recurrent neural networks to …
Processing (NLP). Our system combines convolution and recurrent neural networks to …
Multi-lingual opinion mining on YouTube
In order to successfully apply opinion mining (OM) to the large amounts of user-generated
content produced every day, we need robust models that can handle the noisy input well yet …
content produced every day, we need robust models that can handle the noisy input well yet …
Biomedical vocabulary alignment at scale in the UMLS metathesaurus
With 214 source vocabularies, the construction and maintenance process of the UMLS
(Unified Medical Language System) Metathesaurus terminology integration system is costly …
(Unified Medical Language System) Metathesaurus terminology integration system is costly …
[PDF][PDF] Supersense embeddings: A unified model for supersense interpretation, prediction, and utilization
Coarse-grained semantic categories such as supersenses have proven useful for a range of
downstream tasks such as question answering or machine translation. To date, no effort has …
downstream tasks such as question answering or machine translation. To date, no effort has …
Towards a seamless integration of word senses into downstream NLP applications
Lexical ambiguity can impede NLP systems from accurate understanding of semantics.
Despite its potential benefits, the integration of sense-level information into NLP systems has …
Despite its potential benefits, the integration of sense-level information into NLP systems has …