MatrixFactorization
Related Papers
Discriminative Improvements to Distributional Sentence Similarity
Matrix and tensor factorization have been ap- plied to a number of semantic relatedness tasks, including paraphrase identification. The key idea is that similarity in the latent space implies semantic …...
Relation Extraction with Matrix Factorization and Universal Schemas
Traditional relation extraction predicts rela- tions within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, …...
Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization
Community question answering (CQA) has become an increasingly popular re- search topic. In this paper, we focus on the problem of question retrieval. Question retrieval in CQA can automatically find …...
A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution
Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)- based methods. However some mod- els are opaque to probabilistic interpre- tation, and MF-based …...
CORE: Context-Aware Open Relation Extraction with Factorization Machines
We propose CORE, a novel matrix fac- torization model that leverages contextual information for open relation extraction. Our model is based on factorization ma- chines and integrates facts from various …...
Injecting Logical Background Knowledge into Embeddings for Relation Extraction
Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortu- nately, these methods share a shortcoming with all …...
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization
A key challenge for timeline summarization is to generate a concise, yet complete storyline from large collections of news stories. Pre- vious studies in extractive timeline generation are limited in …...
Lifted Rule Injection for Relation Embeddings
Methods based on representation learning cur- rently hold the state-of-the-art in many natural language processing and knowledge base in- ference tasks. Yet, a major challenge is how to efficiently incorporate …...
Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization
AbstractWe presents in this paper our approach for modeling inter-topic preferences of Twitter users: for example, “those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade”. This …...
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
AbstractBERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks. However, a major blocking issue of applying BERT …...