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Constrained Conditional Model
A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints. The constraint can be used as a way to incorporate expressive prior knowledge into the model and bias the assignments made by the learned model to satisfy these constraints. The framework can be used to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. Models of this kind have recently attracted much attention within the natural language processing ( NLP) community. Formulating problems as constrained optimization problems over the output of learned models has several advantages. It allows one to focus on the modeling of problems by providing the opportunity to incorporate domain-specific knowledge as global constraints using a first order language. Using this declarative framework frees the developer from low level feat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Language Processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Constrained Optimization
In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function with respect to some variables in the presence of constraints on those variables. The objective function is either a cost function or energy function, which is to be minimized, or a reward function or utility function, which is to be maximized. Constraints can be either hard constraints, which set conditions for the variables that are required to be satisfied, or soft constraints, which have some variable values that are penalized in the objective function if, and based on the extent that, the conditions on the variables are not satisfied. Relation to constraint-satisfaction problems The constrained-optimization problem (COP) is a significant generalization of the classic constraint-satisfaction problem (CSP) model. COP is a CSP that includes an ''objective function'' to be optimized. Many algorithms are used to handle ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature Engineering
Feature engineering or feature extraction or feature discovery is the process of using domain knowledge to extract features (characteristics, properties, attributes) from raw data. The motivation is to use these extra features to improve the quality of results from a machine learning process, compared with supplying only the raw data to the machine learning process. Process The feature engineering process is: *Brainstorming or testing features * Deciding what features to create * Creating features * Testing the impact of the identified features on the task * Improving your features if needed * Repeat Typical engineered features The following list provides some typical ways to engineer useful features * Numerical transformations (like taking fractions or scaling) * Category encoder like one-hot or target encoder (for categorical data) * Clustering * Group aggregated values * Principal component analysis (for numerical data) * Feature construction : building new "physical" ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Textual Entailment
Textual entailment (TE) in natural language processing is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed ''text'' (''t'') and ''hypothesis'' (''h''), respectively. Textual entailment is not the same as pure logical entailment – it has a more relaxed definition: "''t'' entails ''h''" (''t'' ⇒ ''h'') if, typically, a human reading ''t'' would infer that ''h'' is most likely true. (Alternatively: ''t'' ⇒ ''h'' if and only if, typically, a human reading ''t'' would be justified in inferring the proposition expressed by ''h'' from the proposition expressed by ''t''.) The relation is directional because even if "''t'' entails ''h''", the reverse "''h'' entails ''t''" is much less certain. [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markov Logic Network
A Markov logic network (MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference. Markov logic networks generalize first-order logic, in the sense that, in a certain limit, all unsatisfiable statements have a probability of zero, and all tautologies have probability one. History Work in this area began in 2003 by Pedro Domingos and Matt Richardson, and they began to use the term MLN to describe it. Description Briefly, it is a collection of formulas from first-order logic, to each of which is assigned a real number, the weight. Taken as a Markov network, the vertices of the network graph are atomic formulas, and the edges are the logical connectives used to construct the formula. Each formula is considered to be a clique, and the Markov blanket is the set of formulas in which a given atom appears. A potential function is associated to each formula, and takes the value of one when the formula is true, and zero when ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Domain Knowledge
Domain knowledge is knowledge of a specific, specialized discipline or field, in contrast to general (or domain-independent) knowledge. The term is often used in reference to a more general discipline—for example, in describing a software engineer who has general knowledge of computer programming as well as domain knowledge about developing programs for a particular industry. People with domain knowledge are often regarded as specialists or experts in their field. Knowledge capture In software engineering, ''domain knowledge'' is knowledge about the environment in which the target system operates, for example, software agents. Domain knowledge usually must be learned from software users in the domain (as domain specialists/experts), rather than from software developers. It may include user workflows, data pipelines, business policies, configurations and constraints and is crucial in the development of a software application. Expert's domain knowledge (frequently informal and il ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Structured Prediction
Structured prediction or structured (output) learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than scalar discrete or real values. Similar to commonly used supervised learning techniques, structured prediction models are typically trained by means of observed data in which the true prediction value is used to adjust model parameters. Due to the complexity of the model and the interrelations of predicted variables the process of prediction using a trained model and of training itself is often computationally infeasible and approximate inference and learning methods are used. Applications For example, the problem of translating a natural language sentence into a syntactic representation such as a parse tree can be seen as a structured prediction problem in which the structured output domain is the set of all possible parse trees. Structured prediction is also used in a wide variety of application domains i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Language Processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Role Labeling
In natural language processing, semantic role labeling (also called shallow semantic parsing or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result. It serves to find the meaning of the sentence. To do this, it detects the arguments associated with the predicate or verb of a sentence and how they are classified into their specific roles. A common example is the sentence "Mary sold the book to John." The agent is "Mary," the predicate is "sold" (or rather, "to sell,") the theme is "the book," and the recipient is "John." Another example is how "the book belongs to me" would need two labels such as "possessed" and "possessor" and "the book was sold to John" would need two other labels such as theme and recipient, despite these two clauses being similar to "subject" and "object" functions. History In 1968, the first idea for semantic role labeling was proposed ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Coreference
In linguistics, coreference, sometimes written co-reference, occurs when two or more expressions refer to the same person or thing; they have the same referent. For example, in ''Bill said Alice would arrive soon, and she did'', the words ''Alice'' and ''she'' refer to the same person. Co-reference is often non-trivial to determine. For example, in ''Bill said he would come'', the word ''he'' may or may not refer to Bill. Determining which expressions are coreferences is an important part of analyzing or understanding the meaning, and often requires information from the context, real-world knowledge, such as tendencies of some names to be associated with particular species ("Rover"), kinds of artifacts ("Titanic"), grammatical genders, or other properties. Linguists commonly use indices to notate coreference, as in ''Billi said hei would come''. Such expressions are said to be ''coindexed'', indicating that they should be interpreted as coreferential. When expressions are corefer ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Transliteration
Transliteration is a type of conversion of a text from one writing system, script to another that involves swapping Letter (alphabet), letters (thus ''wikt:trans-#Prefix, trans-'' + ''wikt:littera#Latin, liter-'') in predictable ways, such as Greek → , Cyrillic → , Greek → the digraph , Armenian → or Latin → . For instance, for the Greek language, Modern Greek term "", which is usually Translation, translated as "Greece, Hellenic Republic", the usual transliteration to Latin script is , and the name for Russia in Cyrillic script, "", is Scientific transliteration of Cyrillic, usually transliterated as . Transliteration is not primarily concerned with representing the Phonetics, sounds of the original but rather with representing the characters, ideally accurately and unambiguously. Thus, in the Greek above example, is transliterated though it is pronounced , is transliterated though pronounced , and is transliterated , though it is pronounced (exactly li ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
