Semantic Enrichment - UMKC School of Computing and Engineering

... classifying the concepts of a new terminology using the Semantic Network, this task becomes more manageable. A new concept does not have to be compared with every concept in the Metathesaurus, but only with those UMLS concepts that have the same assignments to semantic types as the new concept. This ...

... classifying the concepts of a new terminology using the Semantic Network, this task becomes more manageable. A new concept does not have to be compared with every concept in the Metathesaurus, but only with those UMLS concepts that have the same assignments to semantic types as the new concept. This ...

Semantic Enrichment - UMKC School of Computing and Engineering

... several hundred thousands of concepts is extremely difficult, even with computational tools. By first classifying the concepts of a new terminology using the Semantic Network, this task becomes more manageable. A new concept does not have to be compared with every concept in the Metathesaurus, but o ...

... several hundred thousands of concepts is extremely difficult, even with computational tools. By first classifying the concepts of a new terminology using the Semantic Network, this task becomes more manageable. A new concept does not have to be compared with every concept in the Metathesaurus, but o ...

WordNet::Similarity - Measuring the Relatedness of Concepts

... of results. There are four modules that provide all of the functionality required by any of the supported measures: PathFinder.pm, ICFinder.pm, DepthFinder.pm, and LCSFinder.pm. PathFinder.pm provides getAllPaths(), which finds all of the paths and their lengths between two input synsets, and getSho ...

... of results. There are four modules that provide all of the functionality required by any of the supported measures: PathFinder.pm, ICFinder.pm, DepthFinder.pm, and LCSFinder.pm. PathFinder.pm provides getAllPaths(), which finds all of the paths and their lengths between two input synsets, and getSho ...

On the realization of asymmetric high radix signed digital

... synaptic strengths of biological neurons. In both cases, some inputs are made more important than others so that they have a greater effect on the processing element as they combine to produce a neural response. Component 2.Summation Function: The first step in a processing element's operation is to ...

... synaptic strengths of biological neurons. In both cases, some inputs are made more important than others so that they have a greater effect on the processing element as they combine to produce a neural response. Component 2.Summation Function: The first step in a processing element's operation is to ...

Ontology Integration Experienced on Medical Terminologies

... before, there exist different kinds of relationship between concepts and categories of the ACC and STS. This forces us to evaluate each relationship and to incorporate its treatment in the semantic enrichment algorithm. If there exists an IS-A relationship between a concept and a category, then a se ...

... before, there exist different kinds of relationship between concepts and categories of the ACC and STS. This forces us to evaluate each relationship and to incorporate its treatment in the semantic enrichment algorithm. If there exists an IS-A relationship between a concept and a category, then a se ...

Document

... information about content: pieces of the document and their relationships. XML more easily accessible to machines because – Every piece of information is described. – Relations are also defined through the nesting structure. – E.g., the tags appear within the
tags, so they describe p ...

... information about content: pieces of the document and their relationships. XML more easily accessible to machines because – Every piece of information is described. – Relations are also defined through the nesting structure. – E.g., the

Text stylometry for chat bot identification and intelligence

... Dr. Imam, I remember the times when you taught me. I did learn a lot from your classes and enjoyed the experience. Dr. Chang, thanks for your advice and support during my course work and research work. I am so grateful for all that you have done. Thank you. Dr. Hardin, your class is a real-life expe ...

... Dr. Imam, I remember the times when you taught me. I did learn a lot from your classes and enjoyed the experience. Dr. Chang, thanks for your advice and support during my course work and research work. I am so grateful for all that you have done. Thank you. Dr. Hardin, your class is a real-life expe ...

Determining if Two Documents are by the Same Author

... were written by the same assailant. In this paper, we propose a solution to the authorship verification problem: determining if two documents were written by the same author. Importantly, we consider cases where the two input documents are not necessarily long. Note that authorship verification is a ...

... were written by the same assailant. In this paper, we propose a solution to the authorship verification problem: determining if two documents were written by the same author. Importantly, we consider cases where the two input documents are not necessarily long. Note that authorship verification is a ...

Ontology learning from text based on multi

... type and the relationships between the resources in the Web. The main international standards organization for the World Wide Web [World Wide Web Consortium (W3C)] spots that Semantic Web technologies can be used in a variety of application areas. For example: in data integration, whereby data in va ...

... type and the relationships between the resources in the Web. The main international standards organization for the World Wide Web [World Wide Web Consortium (W3C)] spots that Semantic Web technologies can be used in a variety of application areas. For example: in data integration, whereby data in va ...

PTE: Predictive Text Embedding through Large-scale

... class-level word co-occurrences. vised text embeddings are generalizable for different tasks but have a weaker predictive power for a particular task. Despite this deficiency, there are still considerable advantages of text embedding approaches comparing to deep neural networks. First, the training ...

... class-level word co-occurrences. vised text embeddings are generalizable for different tasks but have a weaker predictive power for a particular task. Despite this deficiency, there are still considerable advantages of text embedding approaches comparing to deep neural networks. First, the training ...

Automating Operational Business Decisions Using Artificial

... This was followed by a brainstorming session with a group of early adopters representing a small set of customers involved in testing the IFS application suite. The goal of these sessions was to verify whether the ideas brought up during the focus group meetings had real-world relevance, as well as ...

... This was followed by a brainstorming session with a group of early adopters representing a small set of customers involved in testing the IFS application suite. The goal of these sessions was to verify whether the ideas brought up during the focus group meetings had real-world relevance, as well as ...

A Topic-driven Summarization using K-mean

... documents with respect to a given query. The proposed method finds the proximity of documents and query, and later uses this proximity to rank sentences of each document. It is assumed that the document which is nearer to a query might contain more meaning full sentences with respect to the informat ...

... documents with respect to a given query. The proposed method finds the proximity of documents and query, and later uses this proximity to rank sentences of each document. It is assumed that the document which is nearer to a query might contain more meaning full sentences with respect to the informat ...

A Quotient Construction on Markov Chains with

... and has a positive left-inverse, D∗ (v) = kDvk gives 1-1 correspondence between stab (M2 ) and ...

... and has a positive left-inverse, D∗ (v) = kDvk gives 1-1 correspondence between stab (M2 ) and ...

Probabilistic Topic Models - UCI Cognitive Science Experiments

... Many chapters in this book illustrate that applying a statistical method such as Latent Semantic Analysis (LSA; Landauer & Dumais, 1997; Landauer, Foltz, & Laham, 1998) to large databases can yield insight into human cognition. The LSA approach makes three claims: that semantic information can be de ...

... Many chapters in this book illustrate that applying a statistical method such as Latent Semantic Analysis (LSA; Landauer & Dumais, 1997; Landauer, Foltz, & Laham, 1998) to large databases can yield insight into human cognition. The LSA approach makes three claims: that semantic information can be de ...

Machine learning for information retrieval: Neural networks

... documents. Since it is often difficult to accomplish a successful searchat the initial try, it is customary to conduct searches iteratively and reformulate query statements based on evaluation of the previously retrieved documents. One method for automatically generating improved query formulations ...

... documents. Since it is often difficult to accomplish a successful searchat the initial try, it is customary to conduct searches iteratively and reformulate query statements based on evaluation of the previously retrieved documents. One method for automatically generating improved query formulations ...

Probabilistic Latent Variable Model for Sparse

... equations are similar to NMF update equations as we shall point out in Section V. III. S PARSITY IN THE L ATENT VARIABLE M ODEL Sparse coding refers to a representational scheme where, of a set of components that may be combined to compose data, only a small number are combined to represent any part ...

... equations are similar to NMF update equations as we shall point out in Section V. III. S PARSITY IN THE L ATENT VARIABLE M ODEL Sparse coding refers to a representational scheme where, of a set of components that may be combined to compose data, only a small number are combined to represent any part ...

A Review of Machine Learning Algorithms for Text

... documents, appropriate document representation, dimensionality reduction to handle algorithmic issues [1], and an appropriate classifier function to obtain good generalization and avoid over-fitting. Extraction, Integration and classification of electronic documents from different sources and knowle ...

... documents, appropriate document representation, dimensionality reduction to handle algorithmic issues [1], and an appropriate classifier function to obtain good generalization and avoid over-fitting. Extraction, Integration and classification of electronic documents from different sources and knowle ...

A New Entity Salience Task with Millions of Training Examples

... entity ED from the document if the syntactic head token of some mention in MEA matches the head token of some mention in MED . If EA aligns with more than one document entity, we align it with the document entity that appears earliest. In general, aligning an abstract to its source document is diffi ...

... entity ED from the document if the syntactic head token of some mention in MEA matches the head token of some mention in MED . If EA aligns with more than one document entity, we align it with the document entity that appears earliest. In general, aligning an abstract to its source document is diffi ...

... these collections, and also aims to create tools (dictionaries, workflows, and databases) to support scholarly research at libraries and museums. Much like our team, these organizations lack the resources to manually transcribe their collections or contract with commercial OCR services (e.g., Prime ...

Creating a Knowledge Base From a Collaboratively Generated

... on-line encyclopedia developed by a large amount of users, namely Wikipedia. That is, we are interested in whether and how Wikipedia can be integrated into ...

... on-line encyclopedia developed by a large amount of users, namely Wikipedia. That is, we are interested in whether and how Wikipedia can be integrated into ...

WWW-newsgroup-document Clustering by Means of

... is the number of different words isolated from all documents. During stemming all words in initial model are replaced by their respective stems (a stem is a portion of a word left after removing its suffixes and prefixes). As a result of that, V SM(nstem ×L) is obtained where nstem < nini . During s ...

... is the number of different words isolated from all documents. During stemming all words in initial model are replaced by their respective stems (a stem is a portion of a word left after removing its suffixes and prefixes). As a result of that, V SM(nstem ×L) is obtained where nstem < nini . During s ...

Probabilistic Sense Sentiment Similarity through Hidden Emotions

... w in the relative ratings. To improve the efficiency of enriched matrix, the columns corresponding to each word in the matrix are multiplied by its confidence value. ...

... w in the relative ratings. To improve the efficiency of enriched matrix, the columns corresponding to each word in the matrix are multiplied by its confidence value. ...

TagSpace: Semantic Embeddings from Hashtags

... 167 posts that she has expressed previous positive interactions with (likes, clicks, etc.). Given the person’s trailing n − 1 posts, we use our models to predict the n’th post by ranking it against 10,000 other unrelated posts, and measuring precison and recall. The score of the nth post is obtained ...

... 167 posts that she has expressed previous positive interactions with (likes, clicks, etc.). Given the person’s trailing n − 1 posts, we use our models to predict the n’th post by ranking it against 10,000 other unrelated posts, and measuring precison and recall. The score of the nth post is obtained ...

query expansion using wordnet with a logical model - CiTIUS

... elsewhere[2,3]. In this model documents and queries are represented as Propositional Logic Formula which are constructed from an alphabet of terms using the logical connectives ∧ (conjunction), ∨ (disjunction) and ¬ (negation). Initially these formulas could have any form but for efficiency reasons ...

... elsewhere[2,3]. In this model documents and queries are represented as Propositional Logic Formula which are constructed from an alphabet of terms using the logical connectives ∧ (conjunction), ∨ (disjunction) and ¬ (negation). Initially these formulas could have any form but for efficiency reasons ...

Clustering by weighted cuts in directed graphs

... We need to show that the columns of T 1/2 X T̂ −1/2 are in the space spanned by Y . As Y = T 1/2 X Ẑ −1 , it is sufficient to show that Ẑ −1 T̂ −1/2 is a unitary matrix, or equivalently, that (Ẑ −1 T̂ −1/2 )−1 = T̂ 1/2 Ẑ is unitary. (T̂ 1/2 Ẑ)T T̂ 1/2 Ẑ = Ẑ T T̂ Ẑ = Ẑ T (X T T X)Ẑ = Z T S, ...

... We need to show that the columns of T 1/2 X T̂ −1/2 are in the space spanned by Y . As Y = T 1/2 X Ẑ −1 , it is sufficient to show that Ẑ −1 T̂ −1/2 is a unitary matrix, or equivalently, that (Ẑ −1 T̂ −1/2 )−1 = T̂ 1/2 Ẑ is unitary. (T̂ 1/2 Ẑ)T T̂ 1/2 Ẑ = Ẑ T T̂ Ẑ = Ẑ T (X T T X)Ẑ = Z T S, ...