scholarly journals Semantic role induction in Persian: An unsupervised approach by using probabilistic models

2014 ◽  
Vol 31 (1) ◽  
pp. 181-203 ◽  
Author(s):  
Parisa Saeedi ◽  
Heshaam Faili ◽  
Azadeh Shakery
Keyword(s):  
Probabilistic Models ◽  
Semantic Role ◽  
Role Induction ◽  
Unsupervised Approach

Multiplicative Representations for Unsupervised Semantic Role Induction

2016 ◽  
Author(s):  
Yi Luan ◽  
Yangfeng Ji ◽  
Hannaneh Hajishirzi ◽  
Boyang Li
Keyword(s):  
Semantic Role ◽  
Role Induction

scholarly journals Similarity-Driven Semantic Role Induction via Graph Partitioning

2014 ◽  
Vol 40 (3) ◽  
pp. 633-669 ◽  
Author(s):  
Joel Lang ◽  
Mirella Lapata
Keyword(s):  
Language Processing ◽  
Graph Partitioning ◽  
Clustering Algorithms ◽  
Training Data ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Unique Role ◽  
Role Induction ◽  
Partitioning Problem ◽  
Better Than

As in many natural language processing tasks, data-driven models based on supervised learning have become the method of choice for semantic role labeling. These models are guaranteed to perform well when given sufficient amount of labeled training data. Producing this data is costly and time-consuming, however, thus raising the question of whether unsupervised methods offer a viable alternative. The working hypothesis of this article is that semantic roles can be induced without human supervision from a corpus of syntactically parsed sentences based on three linguistic principles: (1) arguments in the same syntactic position (within a specific linking) bear the same semantic role, (2) arguments within a clause bear a unique role, and (3) clusters representing the same semantic role should be more or less lexically and distributionally equivalent. We present a method that implements these principles and formalizes the task as a graph partitioning problem, whereby argument instances of a verb are represented as vertices in a graph whose edges express similarities between these instances. The graph consists of multiple edge layers, each one capturing a different aspect of argument-instance similarity, and we develop extensions of standard clustering algorithms for partitioning such multi-layer graphs. Experiments for English and German demonstrate that our approach is able to induce semantic role clusters that are consistently better than a strong baseline and are competitive with the state of the art.

Management of Economics and State "Bottom"

2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Probabilistic Models ◽  
Human Life ◽  
Special Software ◽  
Human Quality ◽  
Relationship Of ◽  
The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

AGENT AND EXPERIENCER IN PUDARNYA PESONA CLEOPATRA NOVEL BY HABIBURRAHMAN EL SHIRAZY: A STUDY OF SEMANTIC ROLE

CALL ◽  
2020 ◽  
Vol 2 (1) ◽  
Author(s):  
R. Myrna Nur Sakinah ◽  
Khaerunnisa Siti Latifah ◽  
Jenny Rahmi Nuraeni
Keyword(s):  
Qualitative Research ◽  
Semantic Role ◽  
The Novel ◽  
Data Source ◽  
Participant Roles

This research purposes at describing the roles of semantic study precisely the roles of agent and experiencer in Pudarnya Pesona Cleopatra novel written by Habiburrahman El Shirazy. The research conducted by the writer is qualitative research. The data of this study are agent and experiencer roles that the data source is taken from Pudarnya Pesona Cleopatra novel written by Habiburrahman El Shirazy published in 2003. The method that is used by the writer to collect the data is documentation with the steps: (1) figure out the sentences that contain agent and experiencer in that novel, (2) classify the types of sentences by investigating the novel. In analyzing data, the writer used Saeed’s theory of participant roles for the major theory. The result of this study shows that there are seventeen patterns that are classified into two roles. They are ten sentences of the agent and seven sentences of the experiencer.Keywords: Semantic, Participant Roles, Agent, Experiencer 

Chinese Semantic Role Labeling Based on Feature Combination

2011 ◽  
Vol 22 (2) ◽  
pp. 222-232 ◽  
Author(s):  
Shi-Qi LI ◽  
Tie-Jun ZHAO ◽  
Han-Jing LI ◽  
Peng-Yuan LIU ◽  
Shui LIU
Keyword(s):  
Semantic Role ◽  
Feature Combination ◽  
Semantic Role Labeling

PROBABILISTIC MODELS OF SPECIES DISCOVERY AND BIODIVERSITY COMPARISONS

2016 ◽  
Author(s):  
Stewart M. Edie ◽  
◽  
Peter D. Smits ◽  
David Jablonski
Keyword(s):  
Probabilistic Models ◽  
Species Discovery
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