F1-score
Multi label Text Categorization with Model Combination based on F1 score Maximization
... score for WIPO. As shown in Table 1, there were more category label combinations for JPAT than for Reuters or WIPO. As a result, there were fewer data samples for the same category label assignment for JPAT. ...
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Deep Neural Networks for Syntactic Parsing of Morphologically Rich Languages
... In this paper, we proposed to extend the parser introduced in (Legrand and Collobert, 2015) by learning morphological embeddings. We take ad- vantage of a recursive procedure to propagate mor- phological information ...
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Classification of Google Play Store Application Reviews Using Machine Learning
... Android application. The Android application users download these applications for their personal use. Each user of the application has their own experience with the application. Users download and use these applications ...
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Active Learning for Financial Investment Reports
... The highest F1-score of 0.77 is achieved using margin uncertainty sampling with 747 labelled in- stances. Comparatively, the highest baseline score is 0.76 and requires 1216 labelled instances. The ...
8
NLP@UIOWA at SemEval 2019 Task 6: Classifying the Crass using Multi windowed CNNs
... Subtask A Results. As our system only trained on the provided gold standard, this data set was used to gauge the effectiveness of our two sys- tems. Five fold cross validation was used for pre- dicting training data. The ...
8
Predicting Bitcoin Prices using Convolutional Neural Network Algorithm
... The unnormalized bases are kept in order to get the original values back for the testing data. This is necessary to compare the model's predictions of prices with the true prices. After normalization, the first 90% of ...
6
Connecting targets to tweets : semantic attention based model for target specific stance detection
... macro-average F1 score, on the bench- mark target-specific Stance Detection dataset of tweets, for both the scenario when separate classifiers are allowed for different targets and the scenario when only one ...
16
Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications
... Here we assess Generalized Linear Models (GLMs) to estimate the processing time and the F1 score of face detectors. GLM [64] is a flexible generalization of ordinary linear regression models that assume ...
21
mhirano at the FinSBD Task: Pointwise Prediction Based on Multi layer Perceptron for Sentence Boundary Detection
... In this paper, we presented the application approach of point- wise prediction to sentence boundary detection in the PDF Noisy Text in the financial domain for the FinSBD 2019 shared task. Our point prediction model ...
6
Supervised Word Level Metaphor Detection: Experiments with Concreteness and Reweighting of Examples
... The first finding of note is that the optimal weight- ing for the “metaphor” class is lower than the auto- weight. For example, given that metaphors con- stitute 11-12% of instances in the essay data, the auto-weighting ...
10
Filtering Aggression from the Multilingual Social Media Feed
... In (Burnap and Williams, 2015), authors explore cyber hate on Twitter. They have collected tweets for the specific domain in a two-week time window. Collection of 450,000 tweets was annotated as hateful or genuine. They ...
9
Abstractive Timeline Summarization
... We also report ROUGE concat, where we con- catenate all entries in gold and system timeline and compute ROUGE between the results discarding all date information. While this measure is sub- optimal for TLS (Martschat and ...
11
Initial Experiments In Cross Lingual Morphological Analysis Using Morpheme Segmentation
... lemmatisation F1-score in the VarDial 2019 Shared Task on Cross-Lingual Morphological Analysis for both Karachay-Balkar (Turkic languages, agglutinative morphology) and Sardinian (Romance languages, ...
9
Machine Learning Approaches to Predict Default of Credit Card Clients
... When each layer has only 8 neurons, using dropout causes decrease in accura- cies and increase in f1-scores at the beginning, but as the dropout rate becomes 0.3, f1-score decreases too. Dense(16) → ...
11
Stacked Sentence Document Classifier Approach for Improving Native Language Identification
... Table 8 reports the performances of the stan- dalone sentence classifier on the L1 sentence clas- sification task. For each dataset we report a base- line result calculated by using only word unigrams features. We have ...
8
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
... 1 The F1 score, precision, recall, and AUPR where tools are sorted by decreasing mean F1 score across datasets with available truth sets for taxonomic classifications at the a genus 35 d[r] ...
19
Iterative Language Model Adaptation for Indo Aryan Language Identification
... Iterative language model adaptation basically means that the process for language model adaptation is restarted after one learning epoch. We noted the time it took to produce the results on the second run and decided to ...
10
Handwritten Digit Classification using Machine Learning Models
... The below table 7 shows the precision, recall and f1 score values obtained for the trained data set using the Support Vector Machine Classifier.. Table 7: Precision, Recall and F1 score [r] ...
5
Zeyad at SemEval 2019 Task 6: That’s Offensive! An All Out Search For An Ensemble To Identify And Categorize Offense in Tweets
... This is the body of all the work. We try every possible combination of pre-processing, vectoriza- tion and classification to ensure the output has the best possible F1-score for the given subtask. We start ...
6
Mawdoo3 AI at MADAR Shared Task: Arabic Fine Grained Dialect Identification with Ensemble Learning
... In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1). We propose an ensemble model of a group of ...
5