We present a new local entity disambiguation system. The key to our system is a novel approach for learning entity representations. In our approach we learn an entity aware extension of Embedding for Language Model (ELMo) which we call Entity-ELMo (E-ELMo). Given a paragraph containing one or more named entity mentions…
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48 results for “entity disambiguation”
Improves biomedical entity linking with latent type modeling.
problem Lack of fine-grained type information for entity disambiguation.
method Jointly models entity disambiguation and latent type learning without direct supervision.
result Significant performance improvements over state-of-the-art techniques.
Efficient autoregressive entity linking with correction for faster, more accurate results.
problem High computational cost and non-parallelizable decoding in autoregressive entity linking.
method Parallelizes autoregressive linking across all mentions, uses a shallow decoder, and adds a discriminative correction term.
result 70 times faster and more accurate than previous methods, outperforming state-of-the-art approaches.
Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph const…
FONDUE identifies ambiguous nodes in networks for better analysis.
problem Ambiguous nodes in network data.
method Network embedding for node disambiguation.
result FONDUE outperforms existing methods in ambiguous node identification.
Following a particular news story online is an important but difficult task, as the relevant information is often scattered across different domains/sources (e.g., news articles, blogs, comments, tweets), presented in various formats and language styles, and may overlap with thousands of other stories. In this work we …
We consider a scenario where an artificial agent is reading a stream of text composed of a set of narrations, and it is informed about the identity of some of the individuals that are mentioned in the text portion that is currently being read. The agent is expected to learn to follow the narrations, thus disambiguating…
We present an LDA approach to entity disambiguation. Each topic is associated with a Wikipedia article and topics generate either content words or entity mentions. Training such models is challenging because of the topic and vocabulary size, both in the millions. We tackle these problems using a novel distributed infer…
This article reviews entity resolution methods and their applications.
problem Integrating information from multiple sources to clean and accurately link records.
method Clustering, semi- and fully supervised methods, canonicalization.
result Modern probabilistic record linkage has been foundational.
GENRE retrieves entities autoregressively, improving efficiency and accuracy.
problem Retrieving entities from queries efficiently and accurately.
method Autoregressive generation of entity names, reducing memory footprint and improving context encoding.
result Significantly improved performance on entity disambiguation, linking, and retrieval tasks.
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficultycs.CL
Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where t…
In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic …
EviTrack improves sequential prediction in delayed disambiguation scenarios.
problem Challenges in sequential prediction with delayed disambiguation where early observations are ambiguous.
method EviTrack operates over latent trajectories, applying evidence- and likelihood-ratio-based selection to delay commitment until supported by data.
result EviTrack outperforms sampling-based baselines in a controlled synthetic benchmark, achieving faster post-disambiguation recovery.
MedCAT extracts valuable medical information from unstructured text.
problem Extracting structured information from unstructured biomedical documents.
method Unsupervised machine learning for disambiguation of entities.
result Improved entity detection and linking compared to existing tools.
Paper proposes an algorithm to recover full supervision from weakly labeled data.
problem Machine learning requires expensive data annotation, motivating the use of weak supervision.
method The paper introduces a disambiguation principle and an empirical disambiguation algorithm for partial labelling.
result The algorithm achieves exponential convergence rates under learnability assumptions.
This paper describes a conditional neural network architecture for Mandarin Chinese polyphone disambiguation. The system is composed of a bidirectional recurrent neural network component acting as a sentence encoder to accumulate the context correlations, followed by a prediction network that maps the polyphonic charac…
A new method for name disambiguation in academic networks using multi-view attention and recurrent neural networks.
problem Disambiguating authors with the same name in large-scale academic networks.
method Multi-view Attention-based Pairwise Recurrent Neural Network (MA-PairRNN) that divides papers into blocks based on author attributes and merges blocks of the same author.
result MA-PairRNN significantly improves name disambiguation performance on real-world datasets.
Improves medical note processing by training model on related concepts and global context.
problem Scarce and imbalanced labeled training data limits generalizability of automated abbreviation disambiguation models.
method Data augmentation using related medical concepts and global context information within medical notes.
result Model accuracy improved by almost 14% on CASI dataset and 4% on i2b2 dataset.
A coloring scheme improves graph neural networks for node disambiguation.
problem Improving graph neural networks' ability to distinguish identical node attributes.
method Introducing a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate node attributes.
result CLIP is a universal approximator of continuous functions on graphs with node attributes.
Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publica…
We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task. We explore the use of metric learning techniques to learn artist embeddings directly from audio, and using a dedicated homonym artists dataset, we compare our method with a recent approach that lea…
DivDis learns diverse hypotheses from underspecified data to improve robustness.
problem Learning from underspecified datasets leads to multiple equally viable solutions, causing out-of-distribution issues.
method DivDis framework: 1) learns diverse hypotheses using unlabeled test data, 2) selects one hypothesis with minimal additional supervision.
result DivDis finds robust features in image and natural language processing problems.
Geometry of Polysemycs.CL
Vector representations of words have heralded a transformational approach to classical problems in NLP; the most popular example is word2vec. However, a single vector does not suffice to model the polysemous nature of many (frequent) words, i.e., words with multiple meanings. In this paper, we propose a three-fold appr…
Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.
problem Disambiguating local and global modes in spatiotemporal data.
method Sparse-mode DMD with sparsity-promoting regularization.
result Explicitly constructs discrete and continuous spectra.
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
This work addresses the problem of author name homonymy in the Web of Science. Aiming for an efficient, simple and straightforward solution, we introduce a novel probabilistic similarity measure for author name disambiguation based on feature overlap. Using the researcher-ID available for a subset of the Web of Science…
KEEN Universe provides reproducible and transferable knowledge graph embeddings.
problem Lack of reproducibility and transferability in KGE experiments.
method Developed an ecosystem with Python packages for reproducible and transferable KGEs.
result KEEN Universe facilitates sharing of trained KGE models across different fields.
In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem. FOFE enables us to encode variable-length sequence of words into a theoretically unique fixed-size representation that can be fed into a feed forward neural network (FFNN),…
This paper explores how LLMs can improve pipeline-based conversational agents.
problem Limitations of pipeline-based conversational agents in human-like conversations.
method Investigated LLMs' capabilities in two phases: design and development, and operations.
result LLMs can enhance pipeline-based agents in various tasks like data generation, intent classification, and auto-correction.
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However,…
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encod…
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports. To mine these data properly, attributable to their innate ambiguity, a Word Sense Disambiguation (WSD) algorithm can avoid numbers…
Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently enti…
Survey on ML advances for personalized prediction considering entity characteristics.
problem Sub-optimal performance in personalized prediction due to heterogeneity in data across entities.
method Organized current literature on entity-aware modeling based on characteristics availability and training data amount.
result Recent innovations in uncertainty quantification, fairness, and knowledge-guided machine learning can improve entity-aware modeling.
One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is the task of computationally identifying the right sense of a polysemous word based on its context. Resolving the WSD problem boosts the accuracy of many NLP focused algorithms such as text classification…
MultiImport infers node importance from multiple KG signals.
problem Inferring node importance in a knowledge graph from multiple input signals.
method End-to-end latent variable model using attentive graph neural networks.
result MultiImport consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100.
Neural production systems learn visual dynamics by applying rule templates to entities.
problem Modeling interactions among entities in structured visual environments.
method Inspired by production systems, the paper uses rule templates to bind placeholder variables to specific entities, scoring and applying the best fitting rules to update entity properties.
result The architecture achieves robust future-state prediction and extrapolation from simple to complex environments, outperforming GNNs.
Study benchmarks embedding-based entity alignment methods for KGs.
problem Align entities across different KGs using embeddings.
method Survey and categorize 23 embedding-based methods, propose new KG sampling algorithm, develop open-source library.
result Understood strengths and limitations of embedding-based methods.
Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easy-to-use named-entity recognition tool…
OP3 models entities for better task generalization in reinforcement learning.
problem Generalizing to unseen physical tasks with combinatorial complexity.
method Object-centric perception, prediction, and planning (OP3) framework.
result OP3 outperforms oracle models and state-of-the-art video prediction models.
Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition. Motivated by this fact, we leverage machine translation to improve annotation-projection approaches to cross-lingual named entity recognition…
mGENRE improves multilingual entity linking with autoregressive sequence prediction.
problem Multilingual Entity Linking (MEL) task of resolving language-specific mentions to a multilingual Knowledge Base.
method Autoregressive sequence-to-sequence system that cross-encodes mention strings and entity names.
result Over 50% improvement in average accuracy in zero-shot settings.
This research evaluates and introduces new heuristics for clustering Bitcoin blockchain entities.
problem Efficiently analyzing the vast number of Bitcoin blockchain entities.
method Examined and introduced four new heuristics for clustering Bitcoin blockchain entities.
result Introduced clustering ratio to measure heuristic effectiveness.
We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data. Our model can factorize any set of entities and relations that can be represented by a relational model, including tensors and also multiple relations for each entity. Macau can also incorporate side information, specificall…
Enhances machine learning for dynamic, interconnected entities.
problem Lack of systematic feature engineering for dynamic, interconnected entities.
method Augments current graph machine learning with comprehensive feature engineering in space and time.
result Improves supervised learning on heterogeneous, attributed entities interacting over time.
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets. Unlike traditional machine learning tasks, when an entity resolution algorithm performs well on small hold-out datas…
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performan…
REFIL learns from imagined sub-group interactions to improve multi-agent reinforcement learning.
problem Learning efficiency in multi-agent reinforcement learning settings.
method Randomized Entity-wise Factorization (REFIL) approach.
result REFIL outperforms all strong baselines in challenging multi-task StarCraft settings.