[PDF] Top 20 Semi Supervised Preposition Sense Disambiguation using Multilingual Data
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Semi Supervised Preposition Sense Disambiguation using Multilingual Data
... the multilingual model (pre-trained on the multilingual ...when using both contexts with the 5-window- bag-of-words-based ...when using dependency-based embeddings for both the features and ... See full document
12
A Semi Supervised Feature Clustering Algorithm with Application to Word Sense Disambiguation
... that using unlabeled data can improve the sense disambiguation ...when using LP as the classifier for sense dis- ...labeled data, which makes it robust to the noise in ... See full document
8
Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation
... intended sense of the noun ...a preposition, then it is likely that the intended sense is line of text as in the actor forgot his lines or they read their lines ...a preposition, then it is ... See full document
8
A Semi Supervised Method for Arabic Word Sense Disambiguation Using a Weighted Directed Graph
... tested data and the obtained rate (Precision, Recall, F-Score) given by the voting procedure (VP) and the collocation measures for 127 am- biguous ...each sense we test 40 ...per sense (labeled ... See full document
5
Semi Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
... of semi-supervised learning approaches, co-training and self-training are prob- ably the most ...samples using one (self-training) or two (co-training) classifiers, trained on a smaller set of ... See full document
10
Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity
... one using all automatically annotated examples: Accuracy im- proves by 13 percent points in the coarse evaluation and by 15 percent points in the fine-grained evalua- ...different sense distribution than ... See full document
6
Word Sense Disambiguation by Combining Labeled Data Expansion and Semi Supervised Learning Method
... -based semi-supervised WSD classifier is constructed by combining the discriminative and generative models trained on both labeled and unlabeled samples (See Fujino et ... See full document
10
Word Sense Disambiguation with Multilingual Features
... OntoNotes sense tags (Hovy et ...traditional sense repository encoded in the WordNet lexical ...for supervised learning approaches, WordNet is too fine grained even for human judges to agree (Hovy et ... See full document
10
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
... of sense- annotated data for different languages, a variety of multilingual preprocessing pipelines has also been developed across the years (Padr and Stanilovsky, 2012; Agerri et ...these ... See full document
6
Projecting Parameters for Multilingual Word Sense Disambiguation
... e.g., semi- supervised decision list algorithm (Yarowsky, 1995) and Hyperlex (Véronis Jean, ...WSD using Structural Semantic Interconnections (Roberto Navigli & Paolo Velar- di, 2005) use ... See full document
9
Improving Supervised Sense Disambiguation with Web Scale Selectors
... top supervised systems while our earlier knowledge-based approaches produce results in line with systems not utilizing training ...unannotated data as features into supervised disam- biguation ... See full document
18
Using the Multilingual Central Repository for Graph-Based Word Sense Disambiguation
... when using only supervised information ...only supervised relations is one third of the LKB with all relations (see Table 1), we would expect a heavy coverage penalty on the algorithm, and thus a ... See full document
5
Models and Training for Unsupervised Preposition Sense Disambiguation
... the preposition in has two distinct meanings, namely a temporal and a locative ...improve disambiguation of the words linked by the prepositions (here, morn- ing, shopped, and ...propose using un- ... See full document
6
Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... used semi-supervised learning method for WSD, bootstrapping algorithm works by iteratively classifying unlabeled examples and adding confidently classified examples into labeled dataset using a model ... See full document
8
Semi supervised training of a Kernel PCA Based Model for Word Sense Disambiguation
... training data. Because of the sparse data, the KPCA model wrongly considers the context of the tar- get word to be dissimilar to those previously seen—even though the contexts may in truth be ...unannotated ... See full document
7
Semi supervised Word Sense Disambiguation with Neural Models
... word sense-labeled exam- ...training data, the bidirectional LSTM is small with only 74 + 74 neurons and 100 dimensional word embeddings (K˚ageb¨ack and Sa- lomonsson, ... See full document
12
Partially Supervised Sense Disambiguation by Learning Sense Number from Tagged and Untagged Corpora
... and semi-supervised sense dis- ambiguation methods will mis-tag the in- stances of a target word if the senses of these instances are not defined in sense in- ventories or there are no tagged ... See full document
8
Unsupervised Word Sense Disambiguation with Multilingual Representations
... the supervised multilin- gual WSD evaluations reported in (Banea and Mihal- cea, ...are using the Google Translate API to collect the translations, we have to account for the limitations imposed by this ... See full document
5
Knowledge Rich Word Sense Disambiguation Rivaling Supervised Systems
... Applications using the knowledge contained in Wikipedia include, among others, text categorization (Gabrilovich and Markovitch, 2006), computing semantic similarity of texts (Gabrilovich and Markovitch, 2007; ... See full document
10
Expanding wordnets to new languages with multilingual sense disambiguation
... Since all these approaches use word alignment information, they are not able to generate any translation equivalents for multi-word expressions (MWE). In contrast, our approach use an SMT system trained on a large amount ... See full document
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