dataesr/scientific-paragraphs-categorization huggingface dataset:
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 rs of the stem or word itself, and by adding affixes on stems  [22]. There are m 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 verbal roots usually ratot EVIEW 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 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 ratu/ 2 /j t s b r wun 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 ference collection making it a major impediment to the development of Amharic IR and LP tools. The objective of this research work is: (1) to promote publicly accessible Amaric resources including a standard test collection for IR, morphologically annotated rpora and compiled stopwords list; and (2) to design Amharic IR system by investiating the optimal representation of documents and queries considering both root-based nd stem-based text representations.
Thousands f words can be gener-ated from an Amharic root or its stems by changing the shape of aracters in a stem or root, inserting vowels between root radicals, reduplicating one of e characters of the stem or word itself, and by adding affixes on stems  [22]. There are ultiple stem templates for verbal words. However, all variants have a single root presentation template. The major-ity of words in the language are generated from verbal ots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of ords from verbal roots usually / 1 7 ( /s b r 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 derivation of m verbal roots usually n in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it t 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 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 investithe 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 ents and queries.
Thousands can be gener-ated from an Amharic root or its stems by changing the shape of in a stem or root, inserting vowels between root radicals, reduplicating one of ters of the stem or word itself, and by adding affixes on stems  [22]. There are tem templates for verbal words. However, all variants have a single root tion template. The major-ity of words in the language are generated from verbal ns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of verbal roots usually in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it t 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 ols. The objective of this research work is: (1) to promote publicly accessible Amesources including a standard test collection for IR, morphologically annotated and compiled stopwords list; and (2) to design Amharic IR system by investithe optimal representation of documents and queries considering both root-based m-based text representations.
Thousands s can be gener-ated from an Amharic root or its stems by changing the shape of ers in a stem or root, inserting vowels between root radicals, reduplicating one of racters of the stem or word itself, and by adding affixes on stems  [22]. There are e stem templates for verbal words. 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 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 can be gener-ated from an Amharic root or its stems by changing the shape of in a stem or root, inserting vowels between root radicals, reduplicating one of ters of the stem or word itself, and by adding affixes on stems  [22]. There are stem templates for verbal words. However, all variants have a single root tion template. The major-ity of words in the language are generated from verbal ns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of m verbal roots usually rat 3 of 29 d 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.
Thousands ds can be gener-ated from an Amharic root or its stems by changing the shape of ters in a stem or root, inserting vowels between root radicals, reduplicating one of aracters of the stem or word itself, and by adding affixes on stems  [22]. There are le stem templates for verbal words. However, all variants have a single root entation template. The major-ity of words in the language are generated from verbal Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of from verbal roots usually 'the state of something which is broken'/ 2. /m s b r 3 of 29 ed in scope. The unavailability of full-fledged low-level Amharic NLP tools makes it ult to create annotated corpus automatically. Moreover, there is lack of standard ence collection making it a major impediment to the development of Amharic IR and 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 t lal f 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 n 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 bat 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 w limited in scope. The unavailability of full-fledged low-level Amharic NLP tools ma difficult to create annotated corpus automatically. Moreover, there is lack of stan reference collection making it a major impediment to the development of Amharic I NLP tools.
Thou of words can be gener-ated from an Amharic root or its stems by changing the sha characters in a stem or root, inserting vowels between root radicals, reduplicating o 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 single representation template. The major-ity of words in the language are generated from v roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. The derivat words from verbal roots usually m limited in scope. The unavailability of full-fledged low-level Amharic NLP tools 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. The objective of this research work is: (1) to promote publicly accessi haric resources including a standard test collection for IR, morphologically an corpora and compiled stopwords list; and (2) to design Amharic IR system by gating the optimal representation of documents and queries considering both roo and stem-based text representations.
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. Section 3 discusses related work. In Section 4, we present the pro-posed Amharic IR system architecture along with various options to represent Amharic documents and queries. Section 5 presents the evaluation framework of the proposed Amharic adhoc retrieval system including the presentation of the resources and corpora, experimental setup, and experimental results.
Thousands can be gener-ated from an Amharic root or its stems by changing the shape of in a stem or root, inserting vowels between root radicals, reduplicating one of ters of the stem or word itself, and by adding affixes on stems  [22]. There are stem templates for verbal words. However, all variants have a single root tion template. The major-ity of words in the language are generated from verbal ns, adjectives, and ad-verbs can be derived from verbal roots. The derivation of m verbal roots usually t l mam d m 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 u 'the longest ones'/, the adjective /j b IEW 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 l VIEW 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 ER 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 n 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 n 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 na 'through our money'/, and the functional word /j lelot 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 VIEW 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 hun 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.
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 _ cult to create annotated corpus automatically. Moreover, there is lack of standard rence collection making it a major impediment to the development of Amharic IR and tools. The objective of this research work is: (1) to promote publicly accessible Amc resources including a standard test collection for IR, morphologically annotated ora and compiled stopwords list; and (2) to design Amharic IR system by investing the optimal representation of documents and queries considering both root-based 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 'since'/ is preposition, / 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 / and / limited 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 reference collection making it a major impediment to the development of Amharic IR and NLP tools.