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Found 23 papers, 7 papers with code
Content-Based Top-N Recommendation using Heterogeneous Relations
In this paper, we propose a content-based top-$N$ recommender system by learning the global term weights in profiles.
Leveraging High-Dimensional Side Information for Top-N Recommendation
Top-$N$ recommender systems typically utilize side information to address the problem of data sparsity.
Information Retrieval
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos.
Antonym-Synonym Classification Based on New Sub-space Embeddings
We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper.
Jointly Extracting Multiple Triplets with Multilayer Translation Constraints
Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus.
HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition
To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming.
Collective Entity Alignment via Adaptive Features
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and integration.
Spherically symmetric static black holes in Einstein-aether theory
In particular, we find that, out of the five non-trivial field equations, only three are independent, so the problem is well-posed, as now generically there are only three unknown functions, {$F(r), B(r), A(r)$, where $F$ and $B$ are metric coefficients, and $A$ describes the aether field.}
General Relativity and Quantum Cosmology Astrophysics of Galaxies High Energy Physics - Phenomenology High Energy Physics - Theory
CLEEK: A Chinese Long-text Corpus for Entity Linking
Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus.
Degree-Aware Alignment for Entities in Tail
Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge.
Pre-Trained Models for Heterogeneous Information Networks
We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network.
Joint Event Extraction with Hierarchical Policy Network
Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence.
Reinforcement Learning based Collective Entity Alignment with Adaptive Features
Entity alignment (EA) is the task of identifying the entities that refer to the same real-world object but are located in different knowledge graphs (KGs).
Towards Entity Alignment in the Open World: An Unsupervised Approach
These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment.
Multi-modal Entity Alignment in Hyperbolic Space
To mitigate this problem, a viable approach is to integrate complementary knowledge from other MMKGs.
A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast
Finally, the effectiveness of the proposed method is verified by three wind prediction cases from a wind farm in Liaoning, China.
Discovering Frequency Bursting Patterns in Temporal Graphs
However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph.
The Expressive Power of Graph Neural Networks: A Survey
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications.
Open Knowledge Base Canonicalization with Multi-task Unlearning
MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training.
Temporal Knowledge Question Answering via Abstract Reasoning Induction
In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties.
Open Knowledge Base Canonicalization with Multi-task Learning
MulCanon unifies the learning objectives of these sub-tasks, and adopts a two-stage multi-task learning paradigm for training.
Relation-aware Bidirectional Path Reasoning for Commonsense Question Answering
A bidirectional attention mechanism is applied between the question sequence and the paths that connect entities, which provides us with transparent interpretability.
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training
Secondly, we propose a hybrid selection strategy in the extractor, which not only makes full use of span boundary but also improves the ability of long entity recognition.
