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Sense clustering
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In this paper will introduce an algorithm, which can improve the results of data partitioning with reliability and multiple fuzzifier. This algorithm is named TSSFC. The introduced method includes three steps namely as “labeled data with FCM”, “Data transformation”, and “Semi supervised fuzzy clustering with multiple point fuzzifiers”.
10p
visharma
20-10-2023
8
4
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The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems’ capability to handle abbreviations in clinical narratives.
8p
visteverogers
24-06-2023
7
3
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In recent decades, remote sensing and Geography information of system (GIS) have been able to build thematic maps with high accuracy for managing and monitoring natural resources and the environment, including the solar radiation potential. Establishing solar potential maps from satellite data combined with natural conditions, topography, and land cover will effectively assist in planning solar energy development while helping to identify the appropriate technology and lowest cost.
10p
viironman
02-06-2023
5
3
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This article explores the lexico-semantic network through a brief examination of the English preposition "in" and its equivalent "trong" in Vietnamese. Working within a cognitive linguistic framework, the investigation presents the six clusters of senses of the preposition "in" based on Tyler and Evans’ research, thereby, via the contrastive analysis approach, indicating the similarities and differences in the way speakers of the two languages conceptualize the world via their spatial configuration.
7p
vichristinelagarde
04-07-2022
13
2
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Objectives: The main objective of the dissertation is to research and develop fuzzy clustering techniques on remote sensing image data in order to improve accuracy and improve clustering quality of clustering algorithms.
162p
armyofthedead
23-06-2021
22
3
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In this research, a hybrid approach of fuzzy clustering and particle swarm optimization method based on semi-supervised method for remote sensing imagery analysis (SFCM-PSO) is proposed to overcome the above disadvantages.
16p
trinhthamhodang1218
04-03-2021
13
1
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Twenty six genotypes of cluster bean [Cyamopsis tetragonoloba (L.) Taub] were evaluated to estimate variability, broad sense heritability and genetic advance for pod yield and related attributes, during Rabi 2017. High estimates of GCV and PCV were recorded for number of branches per plant, plant spread at final harvest, fresh pod yield per plant, pod yield per plot, pod yield per hectare, number of pods per cluster, TSS and protein.
6p
cothumenhmong3
22-02-2020
29
0
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The present study was conducted at Research cum Instructional Farm, S.G. College of Agriculture and Research Station, Kumhrawand, Jagdalpur, Bastar, Chhattisgarh, India. One hundred and four rice genotypes lines were planted in three rows in two replication with four checks viz.,MTU 1010, Karma Masuri, Dub- raj Selection and HMTin Randomized Complete Block Design.
10p
nguaconbaynhay3
07-02-2020
16
1
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Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word’s contexts into different classes, each representing a word sense.
9p
bunthai_1
06-05-2013
46
2
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This paper describes an unsupervised knowledge–lean methodology for automatically determining the number of senses in which an ambiguous word is used in a large corpus. It is based on the use of global criterion functions that assess the quality of a clustering solution.
4p
bunthai_1
06-05-2013
43
3
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In this paper we present TroFi (Trope Finder), a system for automatically classifying literal and nonliteral usages of verbs through nearly unsupervised word-sense disambiguation and clustering techniques. TroFi uses sentential context instead of selectional constraint violations or paths in semantic hierarchies. It also uses literal and nonliteral seed sets acquired and cleaned without human supervision in order to bootstrap learning.
8p
bunthai_1
06-05-2013
49
2
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In this paper a novel solution to automatic and unsupervised word sense induction (WSI) is introduced. It represents an instantiation of the ‘one sense per collocation’ observation (Gale et al., 1992). Like most existing approaches it utilizes clustering of word co-occurrences. This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs. The combination with a two-step clustering process using sentence co-occurrences as features allows for accurate results.
8p
bunthai_1
06-05-2013
56
3
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This paper presents an unsupervised algorithm which automatically discovers word senses from text. The algorithm is based on a graph model representing words and relationships between them. Sense clusters are iteratively computed by clustering the local graph of similar words around an ambiguous word. Discrimination against previously extracted sense clusters enables us to discover new senses. We use the same data for both recognising and resolving ambiguity.
4p
bunthai_1
06-05-2013
47
3
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This paper talks about the deciding practical sense boundary of homonymous words. The important problem in dictionaries or thesauri is the confusion of the sense boundary by each resource. This also becomes a bottleneck in the practical language processing systems. This paper proposes the method about discovering sense boundary using the collocation from the large corpora and the clustering methods. In the experiments, the proposed methods show the similar results with the sense boundary from a corpus-based dictionary and sense-tagged corpus. ...
4p
bunbo_1
17-04-2013
46
2
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Recent studies in word sense induction are based on clustering global co-occurrence vectors, i.e. vectors that reflect the overall behavior of a word in a corpus. If a word is semantically ambiguous, this means that these vectors are mixtures of all its senses. Inducing a word’s senses therefore involves the difficult problem of recovering the sense vectors from the mixtures.
4p
bunbo_1
17-04-2013
32
2
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We propose a novel method for inducing monolingual semantic hierarchies and sense clusters from numerous foreign-language-to-English bilingual dictionaries. The method exploits patterns of non-transitivity in translations across multiple languages. No complex or hierarchical structure is assumed or used in the input dictionaries: each is initially parsed into the “lowest common denominator” form, which is to say, a list of pairs of the form (foreign word, English word).
4p
bunbo_1
17-04-2013
46
1
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Báo cáo khoa học: "Learning Word Senses With Feature Selection and Order Identification Capabilities"
This paper presents an unsupervised word sense learning algorithm, which induces senses of target word by grouping its occurrences into a “natural” number of clusters based on the similarity of their contexts. For removing noisy words in feature set, feature selection is conducted by optimizing a cluster validation criterion subject to some constraint in an unsupervised manner. Gaussian mixture model and Minimum Description Length criterion are used to estimate cluster structure and cluster number. ...
8p
bunbo_1
17-04-2013
45
1
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The EM clustering algorithm (Hofmann and Puzicha, 1998) used here is an unsupervised machine learning algorithm that has been applied in many NLP tasks, such as inducing a semantically labeled lexicon and determining lexical choice in machine translation (Rooth et al., 1998), automatic acquisition of verb semantic classes (Schulte im Walde, 2000) and automatic semantic labeling (Gildea and Jurafsky, 2002).
8p
bunbo_1
17-04-2013
42
1
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This paper describes adaptations of unsupervised word sense discrimination techniques to the problem of name discrimination. These methods cluster the contexts containing an ambiguous name, such that each cluster refers to a unique underlying person or place. We also present new techniques to assign meaningful labels to the discovered clusters.
6p
bunbo_1
17-04-2013
33
3
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We discuss Image Sense Discrimination (ISD), and apply a method based on spectral clustering, using multimodal features from the image and text of the embedding web page. We evaluate our method on a new data set of annotated web images, retrieved with ambiguous query terms. Experiments investigate different levels of sense granularity, as well as the impact of text and image features, and global versus local text features.
8p
hongvang_1
16-04-2013
55
1
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