dataesr/scientific-paragraphs-categorization huggingface dataset:
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually _ OR PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools.
Thousands of words can be generated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The majority of words in the language are generated from verbal roots. Nouns, adjectives, and adverbs can be derived from verbal roots. The derivation of words from verbal roots usually ot reference collection making it a major impediment to the development of Amharic IR and NLP tools. The objective of this research work is: (1) to promote publicly accessible Amharic resources including a standard test collection for IR, morphologically annotated corpora and compiled stopwords list; and (2) to design Amharic IR system by investigating the optimal representation of documents and queries considering both root-based and stem-based text representations.
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually b _m _h-w-n_ Appl. Sci. 2022, 12, x FOR PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools.
The major-ity of words in the language are generated from verbal ouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of from verbal roots usually _na j _lela_ R PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools. The objective of this research work is: (1) to promote publicly accessible Amharic resources including a standard test collection for IR, morphologically annotated corpora and compiled stopwords list; and (2) to design Amharic IR system by investigating the optimal representation of documents and queries considering both root-based and stem-based text representations.
Thousands of words can be generated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The majority of words in the language are generated from verbal roots. Nouns, adjectives, and adverbs can be derived from verbal roots. The derivation of words from verbal roots usually limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools.
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually _ 2, x FOR PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools.
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually r-Z-m_ Appl. Sci. 2022, 12, x FOR PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools.
Amharic Writing System Amharic is the working language of the government of Ethiopia currently having estimated population of over 117 million  [34]. It is the second most commonly spok Semitic language in the world after Arabic  [35]. Although many languages are spoken Ethiopia, Amharic is the lingua franca and the most literary language serving as a m dium of instruction in the education system of the country for a long period. Amha uses Ethiopic alphabet and has 34 base characters along with modifications on the r spective base characters. The alphabet is conveniently written in a tabular format seven columns. The first column represents the basic form with vowel ኧ /ə/ and the oth six orders represent modifications with vowels in the order of ኡ /ʔu/, ኢ /ʔi/, ኣ /ʔa/, ኤ /ʔe/ /ʔɨ/, and ኦ /ʔo/.
However, the stems and roots of verbs are different. The details of them are presented in Table  7. limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools. The objective of this research work is: (1) to promote publicly accessible Amharic resources including a standard test collection for IR, morphologically annotated corpora and compiled stopwords list; and (2) to design Amharic IR system by investigating the optimal representation of documents and queries considering both root-based and stem-based text representations. The rest of this paper is organized as follows. Section 2 describes Amharic language and its morphology.
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually involves stem formation followed by word formation. Amharic roots are the base of stems which in turn are the base of many surface forms of words (see Table  1 ). Amharic verbal stems are formed from verbal roots by inserting vowels between radicals (consonants).
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually OR PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools.
Tho of words can be gener-ated from an Amharic root or its stems by changing the sh characters in a stem or root, inserting vowels between root radicals, reduplicating the characters of the stem or word itself, and by adding affixes on stems  [22]. Th multiple stem templates for verbal words. However, all variants have a sing representation template. The major-ity of words in the language are generated from roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The deriva words from verbal roots usually n Appl. Sci. 2022, 12, x FOR PEER REVIEW limited in scope. The unavailability of full-fledged low-level Amharic NLP tools difficult to create annotated corpus automatically. Moreover, there is lack of reference collection making it a major impediment to the development of Amha NLP tools.
T of words can be gener-ated from an Amharic root or its stems by changing the characters in a stem or root, inserting vowels between root radicals, reduplicati the characters of the stem or word itself, and by adding affixes on stems  [22]. multiple stem templates for verbal words. However, all variants have a si representation template. The major-ity of words in the language are generated fro roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The der words from verbal roots usually b Appl. Sci. 2022, 12, x FOR PEER REVIEW limited in scope. The unavailability of full-fledged low-level Amharic NLP too difficult to create annotated corpus automatically. Moreover, there is lack o reference collection making it a major impediment to the development of Amh NLP tools. The objective of this research work is: (1) to promote publicly acce haric resources including a standard test collection for IR, morphologically corpora and compiled stopwords list; and (2) to design Amharic IR system gating the optimal representation of documents and queries considering both and stem-based text representations.
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually W 3 of 29 mited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it ifficult to create annotated corpus automatically. Moreover, there is lack of standard eference collection making it a major impediment to the development of Amharic IR and LP tools.
Th of words can be gener-ated from an Amharic root or its stems by changing the s characters in a stem or root, inserting vowels between root radicals, reduplicatin the characters of the stem or word itself, and by adding affixes on stems  [22]. T multiple stem templates for verbal words. However, all variants have a sin representation template. The major-ity of words in the language are generated from r Appl. Sci. 2022, 12, x FOR PEER REVIEW limited in scope. The unavailability of full-fledged low-level Amharic NLP tool difficult to create annotated corpus automatically. Moreover, there is lack of reference collection making it a major impediment to the development of Amha NLP tools. The objective of this research work is: (1) to promote publicly acces haric resources including a standard test collection for IR, morphologically a corpora and compiled stopwords list; and (2) to design Amharic IR system b gating the optimal representation of documents and queries considering both r and stem-based text representations.
Amharic Writing System Amharic is the working language of the government of Ethiopia currently having estimated population of over 117 million  [34]. It is the second most commonly spok Semitic language in the world after Arabic  [35]. Although many languages are spoken Ethiopia, Amharic is the lingua franca and the most literary language serving as a m dium of instruction in the education system of the country for a long period. Amhar uses Ethiopic alphabet and has 34 base characters along with modifications on the r spective base characters. The alphabet is conveniently written in a tabular format seven columns. The first column represents the basic form with vowel ኧ /ə/ and the oth six orders represent modifications with vowels in the order of ኡ /ʔu/, ኢ /ʔi/, ኣ /ʔa/, ኤ /ʔe/ /ʔɨ/, and ኦ /ʔo/.
The major-ity of words in the language are generated from verbal jectives, and ad-verbs can be derived from verbal roots. The derivation of al roots usually m s b r ols. The objective of this research work is: (1) to promote publicly accessible Amsources including a standard test collection for IR, morphologically annotated and compiled stopwords list; and (2) to design Amharic IR system by investihe optimal representation of documents and queries considering both root-based m-based text representations. e rest of this paper is organized as follows. Section 2 describes Amharic language morphology. Section 3 discusses related work. In Section 4, we present the promharic IR system architecture along with various options to represent Amharic nts and queries. Section 5 presents the evaluation framework of the proposed c adhoc retrieval system including the presentation of the resources and corpora, ental setup, and experimental results.
However, all variants have a single root ntation template. The major-ity of words in the language are generated from verbal ouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of rom verbal roots usually 'fracturing of bone'/ are different though the same concept is expressed via different variants. The top 4 search results for both queries are shown in Table  13. Our text representations differ from Google search engine into two ways: i. Google is based on both basic stems and derived stems. It returns different results for basic stem and derived stem queries though they are semantically similar. However, in our research the stem-based representation is based on basic stems only; we thus acquire the same retrieval results for both basic stems and derived stems.
For example, retrieving using the basic -/s b r 12, x FOR PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools. The objective of this research work is: (1) to promote publicly accessible Amharic resources including a standard test collection for IR, morphologically annotated corpora and compiled stopwords list; and (2) to design Amharic IR system by investigating the optimal representation of documents and queries considering both root-based and stem-based text representations. The rest of this paper is organized as follows. Section 2 describes Amharic language and its morphology.
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually -'to break'/, -/s bab r Appl. Sci. 2022, 12, x FOR PEER REVIEW 3 of 29 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it difficult to create annotated corpus automatically. Moreover, there is lack of standard reference collection making it a major impediment to the development of Amharic IR and NLP tools.
Thousands of words can be gener-ated from an Amharic root or its stems by changing the shape of characters in a stem or root, inserting vowels between root radicals, reduplicating one of the characters of the stem or word itself, and by adding affixes on stems  [22]. There are multiple stem templates for verbal words. However, all variants have a single root representation template. The major-ity of words in the language are generated from verbal roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of words from verbal roots usually -'he is the cause of breaking'/. ii. Google does not employ roots to represent verbs and words derived from them. However, root is the best word form to conflate all variants. We compared the performance of language modeling (LM) and BM25 using the root-based text representation.
Although LM is considered as a strong baseline for IR in different languages, previous Amharic IR research was based on vector space  [25, 26, 55]  but not on LM. We used KL-divergence (LM) with default parameters (i. e. , Dirichlet priority smoothing method (1000) and Interpolate smoothing strategies µ = 2000) and BM25 with the following parameters values: BM25 k1 = 1. 2; BM25 B = 0. 75; BM25 K3 = 1000; TF weighting = 0. 5 and number of feedback = 50. As depicted in Figure  6 , the LM line graph (in red) is above the BM25 (in blue) graph. Both precision and recall values of LM are higher than BM25 at different levels. This is certainly because of the capability of LM to capture words dependency and estimate the probability distribution of a query in each document. LM seems to be better for Amharic language retrieval.
Appl. Sci. 2022,12,  x FOR PEER REVIEW 25 of 2 priority smoothing method (1000) and Interpolate smoothing strategies µ = 2000) and BM25 with the following parameters values: BM25 k1 = 1. 2; BM25 B = 0. 75; BM25 K3 = 1000; TF weighting = 0. 5 and number of feedback = 50. As depicted in Figure6, the LM line graph (in red) is above the BM25 (in blue) graph. Both precision and recall values o LM are higher than BM25 at different levels. This is certainly because of the capability o LM to capture words dependency and estimate the probability distribution of a query in each document. LM seems to be better for Amharic language retrieval.
2 Figure 2. 22 Figure 2. Retrieval effectiveness with and without stopwords. 5. 3. 2. Test Retrieval Results of Word-Based, Stem-Based and Root-Based Retrieval Three experiments were conducted for word-based, stem-based, and root-based re trievals. The aim of the experiments was to investigate the effectiveness of morphologica analysis (i. e. , stem vs. root) on Amharic language. Table9reports the effects of morpho logical analysis on Amharic retrieval effectiveness.
Introduction Nowadays, there are several real systems from different disciplines such as mechanics, electronics, thermal, chemistry, physical, etc. around us. The study, analysis, monitoring, and control of these systems, require as a first step a reasonably accurate mathematical model. Identification is the process by which a simple model structure is derived from a complex system. Experimental measurements consisting of input and output data are recorded from the system at an appropriate sampling period and used in the identification process. For the identification of the DCM parameters, most researchers use classical methods because of their simplicity. However, these methods require a lot of testing on the machine which increases the risk to damage the actual system and they do not give best results, especially when using them to determine the specific inductances (Le, La) and the mechanical parameters (f, J).
It should be noted that all these methods have many advantages for estimation DCM settings. However most of these methods remain difficult to implement and require a large memory for processing information in real time. Several software tools are currently available (MATLAB, LabView, LTspice, etc. ) and can be interfaced to real-world systems via powerful digital signal processing (DSP) and digital microcontroller (dsPIC), dSPACE, etc. ). Several identification tools can be implemented to find a mathematical model (continuous time or discrete time) of a real system. Among these identification tools we can cite: the toolbox, developed for MATLAB, named CONTSID (Continuous-Time System Identification). The use of this toolkit is described in  [12] [13] [14]. Thus, we find the estimation graphical interface and "MATLAB Ident toolbox" complete analysis.
With the availability of all these means, we have decided to search for a new solution for identifying DCM parameters based on classic identification methods (measurement of the rotor resistance and the constant torque) and the identification methods available in MATLAB software (Ident toolbox). This paper is structured as follows: a more general description of the DCM model is presented in section 2. Section 3 summarizes the main classical identification methods used to calculate the DCM parameters. A detailed description of the proposed identification approach for the estimation of the DCM parameters is.
Determination of the inertia To determine the inertia of the DCM, a low-speed test is performed. After the DCM has reached its rated speed, the supply circuit is disconnected, and we measure the time it takes for the DCM to come to a stop. In this part we have detailed the main classical identification methods needed to calculate DCM parameters. We have seen that the identification of electrical parameters (rotor resistance and the torque constant) by these methods remains simple and easy to implement. On the other hand, for the specific inductances and the mechanical parameters (f and J) we did not find any accurate methods for the same parameter, since we obtained different values. So, to simplify the study and get closer to the real model of the DCM we have used the identification toolbox available in MATLAB software.