[HTML][HTML] A reproducible survey on word embeddings and ontology-based methods for word similarity: Linear combinations outperform the state of the art
Human similarity and relatedness judgements between concepts underlie most of cognitive
capabilities, such as categorisation, memory, decision-making and reasoning. For this …
capabilities, such as categorisation, memory, decision-making and reasoning. For this …
An overview of word and sense similarity
R Navigli, F Martelli - Natural Language Engineering, 2019 - cambridge.org
Over the last two decades, determining the similarity between words as well as between
their meanings, that is, word senses, has been proven to be of vital importance in the field of …
their meanings, that is, word senses, has been proven to be of vital importance in the field of …
Simlex-999: Evaluating semantic models with (genuine) similarity estimation
We present SimLex-999, a gold standard resource for evaluating distributional semantic
models that improves on existing resources in several important ways. First, in contrast to …
models that improves on existing resources in several important ways. First, in contrast to …
[PDF][PDF] Don't count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors
Context-predicting models (more commonly known as embeddings or neural language
models) are the new kids on the distributional semantics block. Despite the buzz …
models) are the new kids on the distributional semantics block. Despite the buzz …
RDF2Vec: RDF graph embeddings and their applications
Abstract Linked Open Data has been recognized as a valuable source for background
information in many data mining and information retrieval tasks. However, most of the …
information in many data mining and information retrieval tasks. However, most of the …
[HTML][HTML] Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities
Owing to the need for a deep understanding of linguistic items, semantic representation is
considered to be one of the fundamental components of several applications in Natural …
considered to be one of the fundamental components of several applications in Natural …
Knowledge-enhanced document embeddings for text classification
Accurate semantic representation models are essential in text mining applications. For a
successful application of the text mining process, the text representation adopted must keep …
successful application of the text mining process, the text representation adopted must keep …
[图书][B] Text mining: A guidebook for the social sciences
G Ignatow, R Mihalcea - 2016 - books.google.com
Online communities generate massive volumes of natural language data and the social
sciences continue to learn how to best make use of this new information and the technology …
sciences continue to learn how to best make use of this new information and the technology …
KORE: keyphrase overlap relatedness for entity disambiguation
J Hoffart, S Seufert, DB Nguyen, M Theobald… - Proceedings of the 21st …, 2012 - dl.acm.org
Measuring the semantic relatedness between two entities is the basis for numerous tasks in
IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating …
IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating …
[PDF][PDF] Semeval-2015 task 1: Paraphrase and semantic similarity in twitter (pit)
In this shared task, we present evaluations on two related tasks Paraphrase Identification
(PI) and Semantic Textual Similarity (SS) systems for the Twitter data. Given a pair of …
(PI) and Semantic Textual Similarity (SS) systems for the Twitter data. Given a pair of …