Document Embedding
23 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Document Embedding
unarXive
Latest papers with no code
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.
JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING
TDE obtains document vectors on the fly simultaneously during the jointly learning process of the topical word embeddings.
College Student Retention Risk Analysis From Educational Database using Multi-Task Multi-Modal Neural Fusion
We develop a Multimodal Spatiotemporal Neural Fusion network for Multi-Task Learning (MSNF-MTCL) to predict 5 important students' retention risks: future dropout, next semester dropout, type of dropout, duration of dropout and cause of dropout.
Academic Expert Finding via $(k,\mathcal{P})$-Core based Embedding over Heterogeneous Graphs
Given a user input query and a large amount of academic knowledge (e. g., academic papers), expert finding aims to find and rank the experts who are most relevant to the given query, from the academic knowledge.
Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF
We use a word2vec skip-gram model trained on titles and abstracts of about 70 million scientific articles.
Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling
It can effectively reduce the complexity and meanwhile capture global document context in the modeling of each sentence.
Evaluation of Field-Aware Neural Ranking Models for Recipe Search
Although this requires the specification of bespoke task-dependent models, encouraging empirical results are beginning to emerge.
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions
In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering.
Transformer based Multilingual document Embedding model
One of the current state-of-the-art multilingual document embedding model LASER is based on the bidirectional LSTM neural machine translation model.
Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network
To embed document relevance in topics in the distributed representation, we use a Siamese neural network to jointly learn document representations.
