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Sum-product networks: A new deep architecture
The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are the most general conditions under which the partitionExpand
Representing Text for Joint Embedding of Text and Knowledge Bases
TLDR
A model is proposed that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations, and significantly improves performance over a model that does not share parameters among textual relations with common sub-structure. Expand
Sound and Efficient Inference with Probabilistic and Deterministic Dependencies
TLDR
MC-SAT is an inference algorithm that combines ideas from MCMC and satisfiability, based on Markov logic, which defines Markov networks using weighted clauses in first-order logic and greatly outperforms Gibbs sampling and simulated tempering over a broad range of problem sizes and degrees of determinism. Expand
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
TLDR
A general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction is explored, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Expand
Joint Inference in Information Extraction
TLDR
This paper proposes a joint approach to information extraction, where segmentation of all records and entity resolution are performed together in a single integrated inference process, and is believed to be the first fully joint approach. Expand
Unsupervised Semantic Parsing
TLDR
This work presents the first unsupervised approach to the problem of learning a semantic parser, using Markov logic, and substantially outperforms TextRunner, DIRT and an informed baseline on both precision and recall on this task. Expand
Markov Logic
TLDR
Markov logic accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks, and is the basis of the open-source Alchemy system. Expand
Joint Unsupervised Coreference Resolution with Markov Logic
TLDR
This paper presents the first unsupervised approach that is competitive with supervised ones, made possible by performing joint inference across mentions, in contrast to the pairwise classification typically used in supervised methods, and by using Markov logic as a representation language. Expand
Unsupervised Morphological Segmentation with Log-Linear Models
TLDR
This paper presents the first log-linear model for unsupervised morphological segmentation, based on monolingual features only, which outperforms a state-of-the-art system by a large margin, even when the latter uses bilingual information such as phrasal alignment and phonetic correspondence. Expand
Distant Supervision for Relation Extraction beyond the Sentence Boundary
TLDR
This paper proposes the first approach for applying distant supervision to cross-sentence relation extraction with a graph representation that can incorporate both standard dependencies and discourse relations, thus providing a unifying way to model relations within and across sentences. Expand
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