Research on Topic Relevancy of Sentences Based on HowNet Semantic Computation

2009 ◽  
Author(s):  
Jinzhong Xu ◽  
Jie Liu ◽  
Xiaoming Liu
Keyword(s):  
Semantic Computation ◽  
Topic Relevancy

scholarly journals Effects of topic relevancy and attitude similarity on two measures of affiliation

1971 ◽  
Vol 24 (2) ◽  
pp. 67-68 ◽  
Author(s):  
John J. La Gaipa ◽  
Ron E. Werner
Keyword(s):  
Attitude Similarity ◽  
Two Measures ◽  
Topic Relevancy

Featureless Data Clustering

2010 ◽  
pp. 141-164 ◽  
Author(s):  
Wilson Wong
Keyword(s):  
Data Clustering ◽  
Dominant Role ◽  
Clustering Algorithms ◽  
Adaptive Clustering ◽  
Feature Based ◽  
Clustering Approach ◽  
Semantic Computation ◽  
The Many ◽  
Time Required ◽  
Existing Data

Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However, the applicability of existing data clustering approaches to a wider range of applications is limited due to issues such as complexity involved in semantic computation, long pre-processing time required for feature preparation, and poor extensibility of semantic measurement due to non-incremental feature source. This chapter first summarises the many commonly used clustering algorithms and feature-based semantic measurements, and then highlights the shortcomings to make way for the proposal of an adaptive clustering approach based on featureless semantic measurements. The chapter concludes with experiments demonstrating the performance and wide applicability of the proposed clustering approach.

scholarly journals A New Hypred Improved Method for Measuring Concept Semantic Similarity in WordNet

2019 ◽  
Vol 17 (4) ◽  
pp. 433-439
Author(s):  
Xiaogang Zhang ◽  
Shouqian Sun ◽  
Kejun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Natural Language Processing ◽  
Semantic Similarity ◽  
Language Processing ◽  
Similarity Measures ◽  
Improved Method ◽  
Hierarchical Tree ◽  
Semantic Computation

Computing semantic similarity between concepts is an important issue in natural language processing, artificial intelligence, information retrieval and knowledge management. The measure of computing concept similarity is a fundament of semantic computation. In this paper, we analyze typical semantic similarity measures and note Wu and Palmer’s measure which does not distinguish the similarities between nodes from a node to different nodes of the same level. Then, we synthesize the advantages of measure of path-based and IC-based, and propose a new hybrid method for measuring semantic similarity. By testing on a fragment of WordNet hierarchical tree, the results demonstrate the proposed method accurately distinguishes the similarities between nodes from a node to different nodes of the same level and overcome the shortcoming of the Wu and Palmer’s measure

scholarly journals Height-Relative Determination of (Non-Root) Modal Flavor: Evidence from Hindi

2015 ◽  
pp. 413
Author(s):  
Dave Kush
Keyword(s):  
Attachment Site ◽  
Semantic Type ◽  
Different Types ◽  
Semantic Computation

In this paper it is proposed that modal flavor is determined by the attachment height of a modal. The various interpretations of the Hindi future marker -gaa, which is taken to be a modal, are discussed. The idea put forth is that modal flavor is indirectly constrained by the semantic type of the modal’s prejacent instead of being solely determined via contextual assignment. Modal Bases are re-envisioned as being comprised of different types of alternatives (worlds, world-time pairs, etc.), rather than just sets of worlds determined by different accessibility relations. The correlation between height and attachment site falls out as a consequence of the different types of alternatives a Modal Bases make available for semantic computation.

Semantics in Universal Grammar

2016 ◽  
pp. 182-218
Author(s):  
George Tsoulas
Keyword(s):  
Universal Grammar ◽  
The Core ◽  
Lexical Items ◽  
Semantic Computation ◽  
The Way

This chapter considers the part semantics plays in Universal Grammar. The core cases considered incude principles of semantic computation, the realisation of arguments and the place of truth and reference, especially with respect to anaphora. The chapter highlights the cenral part of Merge in the construction of structure and meaning and extends it to the construction of lexical items and the lexicon where attention is paid not only to the way lexical items are constructed (by Merge) but also the ways in which they are taken apart in derivations again by Merge (self-Merge).

scholarly journals Scalar implicatures in non-monotonic environments

2016 ◽  
Vol 26 ◽  
pp. 205
Author(s):  
Takashi Morita
Keyword(s):  
Empirical Support ◽  
The Other ◽  
Scalar Implicatures ◽  
Negative Component ◽  
Grammatical Analysis ◽  
Other Hand ◽  
Semantic Computation ◽  
Semantic Operators

Scalar implicatures (SIs) have been traditionally analyzed as pragmatic inferences that arise after semantic computation. Recent studies, however, have presented various challenges to this classic analysis; for instance, it has been claimed that SIs can be interpreted within the scope of various semantic operators. These observations motivate a grammatical analysis of SIs: SIs are derived during or before semantic computation. Among various kinds of evidence for the grammati- cal approach to SIs, especially convincing one is SIs embedded in non-monotonic (NM) environments, which post-semantic analyses have difficulty deriving. This paper introduces a new example of NM operators that allow the SI-embedding in their scope, and I thereby provide further empirical support for the grammatical analysis. On the other hand, I will also show that not all NM operators behave in the same way with respect to SI-embedding. It will turn out that Strawson non- monotonicity is required to embed SIs in the negative component of NM envi- ronments; i.e. SI-embedding is unavailable if the NMity can be decomposed into monotonic assertion and monotonic presupposition.

Semantic-Driven Information Recommendation System

2013 ◽  
Vol 475-476 ◽  
pp. 1226-1229
Author(s):  
Zhen Hua Huang ◽  
Qiang Fang
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Recommendation Systems ◽  
User Preference ◽  
Cloud Architecture ◽  
Computation Theory ◽  
Information Recommendation ◽  
Indexing Mechanism ◽  
Semantic Computation ◽  
The One

Information recommendation systems is the one of the most effective tools to solve the problem of information overload. In this paper, we design SIRSCA, a semantic-driven information recommendation system under cloud architecture. SIRSCA mainly includes four modules: semantics representation of foundation data and user preference informations; indexing mechanism of massive semantic informations under cloud architecture; recommendation approaches based on semantic computation theory; and technologies of dynamic migration under cloud architecture.

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