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 l ts' hafiwa 'about the secretary'/ can also be written as /ś 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 l ts' hafiwa/, /s 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 ated from an Amharic root or its stems by changing the shape of root, inserting vowels between root radicals, reduplicating one of m or word itself, and by adding affixes on stems  [22]. There are es for verbal words. However, all variants have a single root. The major-ity of words in the language are generated from verbal , and ad-verbs can be derived from verbal roots. The derivation of usually l ts' hafiwa/, /ś 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 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 l ts' hafiwa/, /s 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 l ts' hafiwa/, /ś limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes difficult to create annotated corpus automatically. Moreover, there is lack of standar reference collection making it a major impediment to the development of Amharic IR an 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 -/, -/nagar Appl. Sci. 2022, 12, x FOR PEER REVIEW limited in scope. The unavailability of full-fledged low-level Amharic NLP tools m difficult to create annotated corpus automatically. Moreover, there is lack of s reference collection making it a major impediment to the development of Amhari 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 _ 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 a estimated population of over 117 million  [34]. It is the second most commonly spoke Semitic language in the world after Arabic  [35]. Although many languages are spoken i 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 o seven columns. The first column represents the basic form with vowel ኧ /ə/ and th other six orders represent modifications with vowels in the order of ኡ / ʔ u/, ኢ /ʔi/, ኣ ʔa/, ኤ /ʔe/, እ /ʔɨ/, and ኦ /ʔo/.
Thousands be gener-ated from an Amharic root or its stems by changing the shape of a stem or root, inserting vowels between root radicals, reduplicating one of s of the stem or word itself, and by adding affixes on stems  [22]. There are templates for verbal words. However, all variants have a single root n template. The major-ity of words in the language are generated from verbal , adjectives, and ad-verbs can be derived from verbal roots. The derivation of erbal roots usually -/), passive ( -/t -/), infinitive ( -/m -/), and reduplicative types of verbal stems. For example, the variants /t d w l 'is called'/, /k m d w lwa 'as soon as she called'/, /b 3 of 29 in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it to create annotated corpus automatically. Moreover, there is lack of standard e collection making it a major impediment to the development of Amharic IR and ols.
Variant Derived Stem Basic Stem /k t g daj in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it to create annotated corpus automatically. Moreover, there is lack of standard e collection making it a major impediment to the development of Amharic IR and ls. 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 investie optimal representation of documents and queries considering both root-based -based text representations. rest of this paper is organized as follows. Section 2 describes Amharic language orphology. 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.
There are stem templates for verbal words. However, all variants have a single root tation 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 om verbal roots usually / -/t g dal 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 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 -/ -/g dal 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 be gener-ated from an Amharic root or its stems by changing the shape of stem or root, inserting vowels between root radicals, reduplicating one of of the stem or word itself, and by adding affixes on stems  [22]. There are templates for verbal words. However, all variants have a single root template. The major-ity of words in the language are generated from verbal adjectives, and ad-verbs can be derived from verbal roots. The derivation of rbal roots usually g d l / -/ R 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 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 'of harmful [p]'/, /g dajot 3 of 29 ited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it ficult to create annotated corpus automatically. Moreover, there is lack of standard erence collection making it a major impediment to the development of Amharic IR and P tools.
' 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. The objective of this research work is: (1) to promote publicly accessible Am-haric resources including a standard test collection for IR, morphologically annotated corpora and compiled stopwords list; and (2) to design Amharic IR system by investi-gating 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.
Amharic Writing System Amharic is the working language of the government of Ethiopia currently having a 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/.
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 t' 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.
Thou 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]. The multiple stem templates for verbal words. However, all variants have a singl 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 s Appl. Sci. 2022, 12, x FOR PEER REVIEW limited in scope. The unavailability of full-fledged low-level Amharic NLP tools m difficult to create annotated corpus automatically. Moreover, there is lack of s reference collection making it a major impediment to the development of Amhari 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 3 of 29 n scope. The unavailability of full-fledged low-level Amharic NLP tools makes it to create annotated corpus automatically. Moreover, there is lack of standard e collection making it a major impediment to the development of Amharic IR and ls.
The derivation of om verbal roots usually n 3 of 29 in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it lt to create annotated corpus automatically. Moreover, there is lack of standard ce collection making it a major impediment to the development of Amharic IR and ools. The objective of this research work is: (1) to promote publicly accessible Amresources including a standard test collection for IR, morphologically annotated a and compiled stopwords list; and (2) to design Amharic IR system by investithe optimal representation of documents and queries considering both root-based em-based text representations. he rest of this paper is organized as follows. Section 2 describes Amharic language s morphology. Section 3 discusses related work. In Section 4, we present the pro-Amharic IR system architecture along with various options to represent Amharic ents and queries.
of words can be gener-ated from an Amharic root or its stems by changing th characters in a stem or root, inserting vowels between root radicals, reduplica 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 representation template. The major-ity of words in the language are generated f roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The de words from verbal roots usually t' Appl. Sci. 2022, 12, x FOR PEER REVIEW limited in scope. The unavailability of full-fledged low-level Amharic NLP t difficult to create annotated corpus automatically. Moreover, there is lack reference collection making it a major impediment to the development of Am NLP tools. The objective of this research work is: (1) to promote publicly ac haric resources including a standard test collection for IR, morphological corpora and compiled stopwords list; and (2) to design Amharic IR system gating the optimal representation of documents and queries considering bot 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 nd 'like'/, /s 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 sk 'up to'/, /w z t 'and so on'/, /go 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 'bravo'/, /wa 'warning'/, /j Appl. Sci. 2022, 12, x FOR PEER REVIEW 3 of 2 limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes i 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.
Thousand of words can be gener-ated from an Amharic root or its stems by changing the shape o characters in a stem or root, inserting vowels between root radicals, reduplicating one o 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 roo representation template. The major-ity of words in the language are generated from verba roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation o words from verbal roots usually l Appl. Sci. 2022, 12, x FOR PEER REVIEW 3 of limited in scope. The unavailability of full-fledged low-level Amharic NLP tools makes difficult to create annotated corpus automatically. Moreover, there is lack of standa reference collection making it a major impediment to the development of Amharic IR an NLP tools.
Amharic Writing System Amharic is the working language of the government of Ethiopia currently having a estimated population of over 117 million  [34]. It is the second most commonly spoke 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/.