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Found 25 papers, 2 papers with code
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing
In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing.
A Large Interlinked Knowledge Graph of the Italian Cultural Heritage
Knowledge is the lifeblood for a plethora of applications such as search, recommender systems and natural language understanding.
Large-scale Taxonomy Induction Using Entity and Word Embeddings
Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies.
ECIR 2020 Workshops: Assessing the Impact of Going Online
In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants.
Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph
In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies.
Multiple Knowledge GraphDB (MKGDB)
MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of open-domain natural language processing applications relying on knowledge bases, such as information extraction, hypernymy discovery, topic clustering, and others.
Enriching Frame Representations with Distributionally Induced Senses
We introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora.
A Framework for Enriching Lexical Semantic Resources with Distributional Semantics
While both kinds of semantic resources are available with high lexical coverage, our aligned resource combines the domain specificity and availability of contextual information from distributional models with the conciseness and high quality of manually crafted lexical networks.
Improving Hypernymy Extraction with Distributional Semantic Classes
In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms.
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7. 5 billion of named entity occurrences in 14. 3 billion sentences from a web-scale crawl of the \textsc{Common Crawl} project.
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.
Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed.
The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links
In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies.
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
On the example of word sense induction and disambiguation (WSID), we show that it is possible to develop an interpretable model that matches the state-of-the-art models in accuracy.
A Large DataBase of Hypernymy Relations Extracted from the Web.
Hypernymy relations (those where an hyponym term shares a {``}isa{''} relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e. g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction.
A New Method for Evaluating Automatically Learned Terminological Taxonomies
Our method assigns a similarity value B{\textasciicircum}i{\_}(l, r) to the learned (l) and reference (r) taxonomy for each cut i of the corresponding anonymised hierarchies, starting from the topmost nodes down to the leaf concepts.
