Markov Logic for Machine Reading
نویسندگان
چکیده
Markov Logic for Machine Reading
منابع مشابه
Machine Reading Using Markov Logic Networks for Collective Probabilistic Inference
DARPA’s Machine Reading project is directed at extracting specific information from natural language text such as events from news articles. We describe a component of FAUST, a system designed for machine reading, which combines stateof-the-art information extraction (IE), based on statistical parsing and local sentencewise analysis, with global article-wide inference using Markov Logic Network...
متن کاملMachine Reading at the University of Washington
Machine reading is a long-standing goal of AI and NLP. In recent years, tremendous progress has been made in developing machine learning approaches for many of its subtasks such as parsing, information extraction, and question answering. However, existing end-to-end solutions typically require substantial amount of human efforts (e.g., labeled data and/or manual engineering), and are not well p...
متن کاملReliability Assessment of Power Generation Systems in Presence of Wind Farms Using Fuzzy Logic Method
A wind farm is a collection of wind turbines built in an area to provide electricity. Wind power is a renewable energy resource and an alternative to non-renewable fossil fuels. In this paper impact of wind farms in power system reliability is investigate and a new procedure for reliability assessment of wind farms in HL1 level is proposed. In proposed procedure, application of Fuzzy – Markov f...
متن کاملExtending Bayesian Logic Programs for Plan Recognition and Machine Reading
Statistical relational learning (SRL) is the area of machine learning that integrates both first-order logic and probabilistic graphical models. The advantage of these formalisms is that they can handle both uncertainty and structured/relational data. As a result, they are widely used in domains like social network analysis, biological data analysis, and natural language processing. Bayesian Lo...
متن کاملInvited Talk: Embedding Probabilistic Logic for Machine Reading
We want to build machines that read, and make inferences based on what was read. A long line of the work in the field has focussed on approaches where language is converted (possibly using machine learning) into a symbolic and relational representation. A reasoning algorithm (such as a theorem prover) then derives new knowledge from this representation. This allows for rich knowledge to capture...
متن کامل