[PDF] Top 20 Evaluation of Machine Learning Methods for Natural Language Processing Tasks
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Evaluation of Machine Learning Methods for Natural Language Processing Tasks
... Feature (subset) selection is the process in which a sub- set of the available predictor features defining the input of the classification task are removed if they can’t be shown to be relevant in solving the ... See full document
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Evaluating unsupervised learning for natural language processing tasks
... in-context evaluation will help as- sess their performance and merits in a more infor- mative ...as language exhibits ambiguity and polysemy, which are rather difficult to capture in a context- independent ... See full document
8
Quantifying Uncertainties in Natural Language Processing Tasks
... Results Test set performances of the models trained with and without uncertainties are listed in Table 3. We observe that much different from the sentiment analysis case, mod- els that quantify data uncertainty improves ... See full document
8
Natural Language Processing Applications in Deep Learning Methods
... accuracy, machine-driven Text Classification makes the classification method quick and a lot of economical since it automatically categorizes document Language is employed as medium for written moreover as ... See full document
6
MAE and MAI: Lightweight Annotation and Adjudication Tools
... of machine learning for natural language processing tasks has been steadily increasing over the years: text processing challenges such as those associated with the SemEval ... See full document
5
Survey on Artificial Intelligence in Healthcare
... data. Machine learning methods, modern deep learning, as well as natural language processing are popular AI ...techniques. Machine learning methods ... See full document
5
Learning Representations for Weakly Supervised Natural Language Processing Tasks
... to machine learning, such as Alternating Structure Optimization (ASO) (Ando and Zhang 2005) and Structural Correspondence Learning (SCL) (Blitzer, McDonald, and Pereira ...prediction tasks ... See full document
36
A Review of Artificial Intelligence in the Internet of Things
... simulate natural selection following a process of evolution of individuals through random ...contain methods such as mutation, crossing and selection to find the best solution to a given ...the ... See full document
12
Analyzing Behavior of Cancer Patients using Machine Learning Techniques
... The Natural Language Processing (NLP) provides a wide variety of tools for text ...and machine learning techniques facilitates many applications ranging from diagnosis, classification ... See full document
10
Scope of Artificial Intelligence in Law
... uses machine learning and natural language processing to analyze document by making parallel use of AI techniques in contract ...legal tasks which are given ... See full document
9
Evaluating Layers of Representation in Neural Machine Translation on Part of Speech and Semantic Tagging Tasks
... in machine translation, it is diffi- cult to interpret what they learn about ...experimental evaluation leads to interesting insights about the hidden representations in NMT models such as the effect of ... See full document
10
Geolocation with Attention Based Multitask Learning Models
... dition: when there are enough classification la- bels. We show this by evaluating on two differ- ent schemes for discretizing coordinates into la- bels. The first (Rahimi et al., 2017b) identifies ir- regular areas via ... See full document
7
Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees
... The benefits of sampling-based learning go be- yond stand-alone parsing. For instance, we can use the framework to correct preprocessing mis- takes in features such as part-of-speech (POS) tags. In this case, we ... See full document
11
Using mention accessibility to improve coreference resolution
... explicitly learning separate ...by learning a model over three versions of each base feature: unprefixed, conjoined with the type of the current mention, and conjoined with concatenation of the types of the ... See full document
6
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
Term Categorization Using Latent Semantic Analysis for Intelligent Query Processing
... categorization. Natural language query processing is the most challenging ...query processing is essential to retrieve the results from the large-scale ...query processing techniques ... See full document
6
Unsupervised Neural Hidden Markov Models
... Part-of-speech tags encode morphosyntactic informa- tion about a language and are a fundamental tool in downstream NLP applications. In English, the Penn Treebank (Marcus et al., 1994) distinguishes 36 cate- ... See full document
9
Learning to Write with Cooperative Discriminators
... The language model immediately offers generic compliments about the breakfast and staff, whereas L2W chooses a rea- sonable but less obvious path, stating that the pre- viously mentioned vouchers were not ... See full document
12
Coarse to Fine Decoding for Neural Semantic Parsing
... In this paper we presented a coarse-to-fine de- coding framework for neural semantic parsing. We first generate meaning sketches which abstract away from low-level information such as argu- ments and variable names and ... See full document
12
Learning Based Single Document Summarization with Compression and Anaphoricity Constraints
... Past work has explored various kinds of struc- ture for summarization. Some work has focused on improving content selection using discourse structure (Louis et al., 2010; Hirao et al., 2013), topical structure (Barzilay ... See full document
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