Semi-supervised
Unbiased Generative Semi-Supervised Learning
... We begin by considering the highly influential work by Castelli and Cover (1995, 1996). This looks not at a particular semi-supervised algorithm, but rather at a slightly more general question of when ...
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Semi Supervised Noun Compound Analysis with Edge and Span Features
... In this paper, we propose the use of spans in addition to edges in noun compound analysis. A span is a sequence of words that can represent a noun compound. Compared with edges, spans have good properties in terms of ...
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Active Deep Networks for Semi Supervised Sentiment Classification
... novel semi- supervised learning algorithm called Ac- tive Deep Networks (ADN), to address the semi-supervised sentiment classifica- tion problem with active ...the ...
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Nonparametric Bayesian Semi supervised Word Segmentation
... Problems and Beyond Unsupervised word seg- mentation with NPYLM works surprisingly well for many languages (Mochihashi et al., 2009); however, it has certain issues. First, since it optimizes the performance of the ...
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Semi supervised Semantic Pattern Discovery with Guidance from Unsupervised Pattern Clusters
... conduct supervised learning, semi-supervised learning (SSL) is particularly attractive to NLP researchers since it only requires a handful of labeled examples, known as ...
9
Model selection for semi-supervised clustering
... One can observe that for the semi-supervised, density- based clustering approach FOSC-OPTICSDend, CVCP leads consistently to a much better performance than the expected performance. The difference is ...
12
Lγ-PageRank for semi-supervised learning
... Graph-based Semi-Supervised Learning (G-SSL) is a modern important tool for classi- ...and Supervised Learning demands extensive labeled examples, G-SSL combines limited tagged examples and the data ...
20
Semi Supervised Semantic Role Labeling
... of semi-supervised learning is widespread in many natural language tasks, rang- ing from parsing to word sense disambiguation, its application to FrameNet-style semantic role label- ing is, to our ...
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Semi Supervised Semantic Role Labeling: Approaching from an Unsupervised Perspective
... Unsupervised learning has been one of the central paradigms for the closely-related area of relation extraction, where several techniques have been proposed to cluster semantically similar verbalizations of relations ...
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Semi Supervised Polarity Lexicon Induction
... Most natural language data has some structure that could be exploited even in the absence of fully an- notated data. For instance, documents are simi- lar in the terms they contain, words could be syn- onyms of each ...
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Statistical Models for Unsupervised, Semi Supervised Supervised Transliteration Mining
... unsupervised, semi-supervised, and supervised translitera- tion ...all semi-supervised and supervised systems that participated in the NEWS 2010 shared ...
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Supervised and Semi-supervised Methods based Organization Name Disambiguity
... In this paper, we probe into the problem of finding related tweets to a given organization. This is a challenging task due to the potential organization name ambiguity. This task is more challenging caused by two ...
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A Semi Supervised Bayesian Network Model for Microblog Topic Classification
... Microblogging services have brought users to a new era of knowledge dissemination and informa- tion seeking. However, the large volume and multi-aspect of messages hinder the ability of users to conveniently locate the ...
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Simple Semi supervised Dependency Parsing
... We present a simple and effective semi- supervised method for training dependency parsers. We focus on the problem of lex- ical representation, introducing features that incorporate word clusters derived ...
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Semi supervised Clustering of Medical Text
... predicted number of clusters by the proposed NSGA-II-clus (internal and external) technique for a par- ticular question. Here as mentioned earlier in Section 2, for each question, we have varied the number of clusters in ...
9
Simple Semi Supervised POS Tagging
... investigate semi-supervised named-entity recognition based on Brown clusters and active ...gate semi-supervised dependency parsing based on Brown ...
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Semi Supervised Learning of Concatenative Morphology
... In this paper, we extend the Morfessor Base- line method for the semi-supervised case. Morfes- sor (Creutz and Lagus, 2002; Creutz and Lagus, 2005; Creutz and Lagus, 2007, etc.) is one of the ...
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Towards Automated Semi-Supervised Learning
... on supervised learning. In many applications, however, semi- supervised learning (SSL) are widespread and current AutoML systems could not well address SSL ...
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A Semi Supervised Approach for Gender Identification
... a Semi-Supervised Learning variant of the k nearest neighbors algorithm that uses small sets of labeled data and a larger amount of unlabeled data to classify the authors of texts by gender (man vs ...
6
Semi Supervised Learning for Relation Extraction
... This paper proposes a new effective and efficient semi-supervised learning method in relation ex- traction. First, a moderate number of weighted support vectors are bootstrapped from all the avail- able ...
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