scholarly journals Contextualized Translations of Phrasal Verbs with Distributional Compositional Semantics and Monolingual Corpora

2019 ◽  
Vol 45 (3) ◽  
pp. 395-421 ◽  
Author(s):  
Pablo Gamallo ◽  
Susana Sotelo ◽  
José Ramom Pichel ◽  
Mikel Artetxe
Keyword(s):  
Vector Space ◽  
Nearest Neighbor ◽  
Word Meaning ◽  
Target Language ◽  
Data Set ◽  
Phrasal Verbs ◽  
Bilingual Dictionary ◽  
Transfer Rules ◽  
Distributional Method ◽  
Syntactic Dependencies

This article describes a compositional distributional method to generate contextualized senses of words and identify their appropriate translations in the target language using monolingual corpora. Word translation is modeled in the same way as contextualization of word meaning, but in a bilingual vector space. The contextualization of meaning is carried out by means of distributional composition within a structured vector space with syntactic dependencies, and the bilingual space is created by means of transfer rules and a bilingual dictionary. A phrase in the source language, consisting of a head and a dependent, is translated into the target language by selecting both the nearest neighbor of the head given the dependent, and the nearest neighbor of the dependent given the head. This process is expanded to larger phrases by means of incremental composition. Experiments were performed on English and Spanish monolingual corpora in order to translate phrasal verbs in context. A new bilingual data set to evaluate strategies aimed at translating phrasal verbs in restricted syntactic domains has been created and released.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Symmetry Breaking and Training from Incomplete Data with Radial Basis Boltzmann Machines

1997 ◽  
Vol 08 (03) ◽  
pp. 301-315 ◽  
Author(s):  
Marcel J. Nijman ◽  
Hilbert J. Kappen
Keyword(s):  
Symmetry Breaking ◽  
Incomplete Data ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Boltzmann Machine ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Input Space ◽  
Learning Rules ◽  
Radial Basis

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.

scholarly journals Determination of Reactivity Ratios from Binary Copolymerization Using the k-Nearest Neighbor Non-Parametric Regression

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3811
Author(s):  
Iosif Sorin Fazakas-Anca ◽  
Arina Modrea ◽  
Sorin Vlase
Keyword(s):  
Experimental Data ◽  
Nearest Neighbor ◽  
Optimization Method ◽  
Reactivity Ratios ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Integration Algorithm ◽  
Data Set ◽  
Parametric Regression ◽  
Non Parametric

This paper proposes a new method for calculating the monomer reactivity ratios for binary copolymerization based on the terminal model. The original optimization method involves a numerical integration algorithm and an optimization algorithm based on k-nearest neighbour non-parametric regression. The calculation method has been tested on simulated and experimental data sets, at low (<10%), medium (10–35%) and high conversions (>40%), yielding reactivity ratios in a good agreement with the usual methods such as intersection, Fineman–Ross, reverse Fineman–Ross, Kelen–Tüdös, extended Kelen–Tüdös and the error in variable method. The experimental data sets used in this comparative analysis are copolymerization of 2-(N-phthalimido) ethyl acrylate with 1-vinyl-2-pyrolidone for low conversion, copolymerization of isoprene with glycidyl methacrylate for medium conversion and copolymerization of N-isopropylacrylamide with N,N-dimethylacrylamide for high conversion. Also, the possibility to estimate experimental errors from a single experimental data set formed by n experimental data is shown.

scholarly journals Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongbin Pan ◽  
Yang Xiang ◽  
Jian Xiong ◽  
Yifan Zhao ◽  
Ziwei Huang ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Detection Method ◽  
Access Point ◽  
Combination Method ◽  
Data Set ◽  
Positioning Accuracy ◽  
Multipath Effects ◽  
Positioning Method ◽  
Fingerprint Database ◽  
Mismatch Rate

Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k -nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

scholarly journals ASPECT BASED SENTIMENT ANALYSIS DATA KUESIONER DI RUMAH SAKIT MUHAMMADIYAH LAMONGAN MENGGUNAKAN ALGORITMA K-NN.

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 506
Author(s):  
Mustain Mustain Mustain
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Vector Space Model ◽  
Analysis Data ◽  
Cosine Similarity ◽  
K Nearest Neighbor ◽  
Space Model

Kesulitan untuk mengorganisir data kuesioner yang bersifat konvensional melatarbelakangi penelitian ini. Oleh karena itu dibuat sistem yang memudahkan pengelompokan data kuesioner secara otomatis yang lengkap dengan sentimen yang terkandung didalamnya. Dataset yang digunakan dalam penelitian ini adalah data kuesioner rumah sakit Muhammadiyah lamongan. Penelitian ini hanya menangani kuesioner yang berbentuk teks. Data dengan fisik kertas direkap kemudian diinput ke database lengkap dengan kategori unit kerja dan sentiment. Selanjutnya dataset tersebut di dilakukan pre-prosesing yang meliputi penanganan negasi case folding, tokenizing, filtering dan stemming. Sebagai data uji komentar dari kuesioner akan dilakukan pre-prosesing selanjutnya dihitung tingkat kemiripan document dengan menggunakan metode K- Nearest Neighbor dan Vector Space Model. Jumlah data yang ditangani mempengaruhi performa system terutama dari akurasi dan kecepatan pada saat proses klasifikasi. Hasil dari sistem yang dibuat berupa ranking dokumen yang paling mirip dengan dataset berdasarkan urutan nilai cosine similarity. Ujicoba klasifikasi berdasarkan kelas kategori menghasilkan nilai akurasi 91 %. Ujicoba berdasarkan Kelas Sentimen sebesar 94 %.dari kombinasi keduanya system berhasil mendapat akurasi sebesar 86 %

scholarly journals A Comprehensive Comparative Study Using Vector Space Model with K-Nearest Neighbor on Text Categorization Data

2007 ◽  
Vol 2 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Wa`el Musa Hadi ◽  
Fadi Thabtah ◽  
Salahideen Mousa ◽  
Samer Al Hawari ◽  
Ghassan Kanaan ◽  
...  
Keyword(s):  
Comparative Study ◽  
Vector Space ◽  
Text Categorization ◽  
Nearest Neighbor ◽  
Vector Space Model ◽  
K Nearest Neighbor ◽  
Space Model

scholarly journals A MODIFIED MODEL BASED ON FLOWER POLLINATION ALGORITHM AND K-NEAREST NEIGHBOR FOR DIAGNOSING DISEASES

2018 ◽  
Vol 19 (1) ◽  
pp. 144-157
Author(s):  
Mehdi Zekriyapanah Gashti
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Critical Role ◽  
Clinical Manifestations ◽  
Flower Pollination Algorithm ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Modified Model ◽  
Flower Pollination

Exponential growth of medical data and recorded resources from patients with different diseases can be exploited to establish an optimal association between disease symptoms and diagnosis. The main issue in diagnosis is the variability of the features that can be attributed for particular diseases, since some of these features are not essential for the diagnosis and may even lead to a delay in diagnosis. For instance, diabetes, hepatitis, breast cancer, and heart disease, that express multitudes of clinical manifestations as symptoms, are among the diseases with higher morbidity rate. Timely diagnosis of such diseases can play a critical role in decreasing their effect on patients’ quality of life and on the costs of their treatment. Thanks to the large data set available, computer aided diagnosis can be an advanced option for early diagnosis of the diseases. In this paper, using a Flower Pollination Algorithm (FPA) and K-Nearest Neighbor (KNN), a new method is suggested for diagnosis. The modified model can diagnose diseases more accurately by reducing the number of features. The main purpose of the modified model is that the Feature Selection (FS) should be done by FPA and data classification should be performed using KNN. The results showed higher efficiency of the modified model on diagnosis of diabetes, hepatitis, breast cancer, and heart diseases compared to the KNN models. ABSTRAK: Pertumbuhan eksponen dalam data perubatan dan sumber direkodkan daripada pesakit dengan penyakit berbeza boleh disalah guna bagi membentuk kebersamaan optimum antara simptom penyakit dan mengenal pasti gejala penyakit (diagnosis). Isu utama dalam diagnosis adalah kepelbagaian ciri yang dimiliki pada penyakit tertentu, sementara ciri-ciri ini tidak penting untuk didiagnosis dan boleh mengarah kepada penangguhan dalam diagnosis. Sebagai contoh, penyakit kencing manis, radang hati, barah payudara dan penyakit jantung, menunjukkan banyak klinikal simptom jelas dan merupakan penyakit tertinggi berlaku dalam masyarakat. Diagnosis tepat pada penyakit tersebut boleh memainkan peranan penting dalam mengurangkan kesan kualiti  hidup dan kos rawatan pesakit. Terima kasih kepada set data yang banyak, diagnosis dengan bantuan komputer boleh menjadi pilihan maju menuju ke arah diagnosis awal kepada penyakit. Kertas ini menggunakan Algoritma Flower Pollination (FPA) dan K-Nearest Neighbor (KNN), iaitu kaedah baru dicadangkan bagi diagnosis. Model yang diubah suai boleh mendiagnosis penyakit lebih tepat dengan mengurangkan bilangan ciri-ciri. Tujuan utama model yang diubah suai ini adalah bagi Pemilihan Ciri (FS) perlu dilakukan menggunakan FPA and pengkhususan data perlu dijalankan menggunakan KNN. Keputusan menunjukkan model yang diubah suai lebih cekap dalam mendiagnosis penyakit kencing manis, radang hati, barah payudara dan penyakit jantung berbanding model KNN.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

PROBLEMS OF LEXICAL EQUIVALENTING OF RUSSIAN PARTICLES IN A BILINGUAL DICTIONARY

2019 ◽  
Vol 19 (4) ◽  
pp. 99-107
Author(s):  
Larisa Alimpieva ◽  
Keyword(s):  
Foreign Language ◽  
Russian Language ◽  
Target Language ◽  
Bilingual Dictionary ◽  
Bilingual Dictionaries ◽  
Communicative Act ◽  
The Russian Language ◽  
Bilingual Lexicography

In the process of communicative act Russian particles concurrently fulfil different functions. It makes Russian particles an important unit of functional-pragmatic sphere of the Russian language which is characterized by its national specifics and connotativity. The problem of codification of Russian particles in bilingual lexicography is complicated. The main problem at compiling a dictionary lemma is filiation (division of meanings) of Russian particles and their rendering by lexical means of a foreign language. The existing lexicographic descriptions of Russian particles in bilingual dictionaries irrelevantly reflect the structure and contents of their meanings. The aim of the article is to consider some theoretical problems of description of Russian particles by means of a second (target) language in dictionary lemmas of bilingual dictionaries.

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