[PDF] Top 20 Evaluating unsupervised learning for natural language processing tasks
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Evaluating unsupervised learning for natural language processing tasks
... on unsupervised PoS tagging men- tioned in the previous section agree on the fact that its evaluation, at least using clustering evaluation measures, is ... See full document
8
Analyzing Behavior of Cancer Patients using Machine Learning Techniques
... machine learning (ML) through OSG (online support group) for cancer care as well as for cancer treatment ...(natural language processing) techniques on unstructured text discussions accrued in ... See full document
10
Learning Structural Kernels for Natural Language Processing
... Our proposed approach for model selection re- lies on Gaussian Processes (GPs) (Rasmussen and Williams, 2006), a widely used Bayesian kernel ma- chine. GPs allow efficient and fine-grained model selection by maximizing ... See full document
14
Fast and Accurate Decision Trees for Natural Language Processing Tasks
... machine learning algorithms, data sparse- ness combined with noise will likely yield over- fitted models, which means that the constructed tree will model a features/label combination that will never exists in ... See full document
8
R grams: Unsupervised Learning of Semantic Units in Natural Language
... of natural languages as segmented by traditional approaches follow a Zipfian distribution (Zipf, ...segmented natural language is comprised of a small number of very high- frequent items, which are ... See full document
11
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
... Natural Language Processing (NLP) applications usually take words as basic input units; therefore, it is important that they be represented in a meaningful ...to Language (HAL) [Lund and ... See full document
10
Convolution Kernels with Feature Selection for Natural Language Processing Tasks
... Most of the results showed that SK achieves its maximum performance when n = 2. The per- formance deteriorates considerably once n exceeds 4. This implies that SK with larger sub-structures degrade classification ... See full document
8
Morphological Paradigms: Computational Structure and Unsupervised Learning
... This thesis explores the computational struc- ture of morphological paradigms from the per- spective of unsupervised learning. Three top- ics are studied: (i) stem identification, (ii) paradigmatic ... See full document
7
Quantifying Uncertainties in Natural Language Processing Tasks
... With advancement of modern machine learning algorithms and systems, they are applied in various applications that, in some scenarios, impact human wellbeing. Many of such al- gorithms learn black-box mappings ... See full document
8
MAE and MAI: Lightweight Annotation and Adjudication Tools
... machine learning for natural language processing tasks has been steadily increasing over the years: text processing challenges such as those associated with the SemEval workshops ... See full document
5
Detecting Social Roles in Twitter
... machine learning ap- proach for detecting social roles from Twitter profiles, which can act as a strong baseline for this ...an unsupervised manner from Twitter ...other natural language ... See full document
7
Learning Representations for Weakly Supervised Natural Language Processing Tasks
... machine learning, such as Alternating Structure Optimization (ASO) (Ando and Zhang 2005) and Structural Correspondence Learning (SCL) (Blitzer, McDonald, and Pereira ...prediction tasks using ... See full document
36
Deep Unsupervised Feature Learning for Natural Language Processing
... deep learning ideal is to train deep, non-linear mod- els over large collections of unlabeled data, and then use these models to automatically extract information-rich, higher-level features 3 to integrate into ... See full document
6
Evaluation of Machine Learning Methods for Natural Language Processing Tasks
... Machine Learning and in most work in statistical ...machine learning (ML) methods (Mooney, 1996) on the task of word sense disam- biguation (WSD) is a good example of best practice in this ...machine ... See full document
6
Incorporating Copying Mechanism in Sequence to Sequence Learning
... (Seq2Seq) learning referred to as copying, in which cer- tain segments in the input sequence are selectively replicated in the output se- ...human language communica- ...Seq2Seq learning and propose ... See full document
10
A Review of Unsupervised Artificial Neural Networks with Applications
... on unsupervised learning techniques and exploitation of the similarities between data [15, 16, ...competitive learning, a process where all the output neurons compete with one ... See full document
5
Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization
... We map the Babel synsets to WordNet 3.0 synsets (Miller, 1995) using the BabelNet API (Navigli and Ponzetto, 2012), and map these synsets to their corresponding WordNet’s super- sense categories (Miller, 1990; Fellbaum, ... See full document
13
Unsupervised Event Coreference for Abstract Words
... 1. We construct a new dataset, derived from the PubMed corpus, by replacing all named entities with their respective NE label. We normalize Proteins, Cell lines, Cell types, RNA and DNA names using the tagger described ... See full document
5
Broad coverage CCG Semantic Parsing with AMR
... two learning challenges: grammar in- duction, which assigns meaning representations to words and phrases, and parameter estimation, where we learn a model for combining these pieces to analyze full ... See full document
12
Proceedings of the IJCNLP 2017, Shared Tasks
... five tasks (and many more registered to participate, but ended up not submitting systems), submitting hundreds of runs for the different tasks and their subtasks: 5 for task 1, 13 for task 2, 3 for task 3, ... See full document
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