dataesr/scientific-paragraphs-categorization huggingface dataset:
Thousands rds can be gener-ated from an Amharic root or its stems by changing the shape of cters 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 ple stem templates for verbal words. However, all variants have a single root sentation 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 s from verbal roots usually / CVCC /f l 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 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 / CVCC /mar 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 k 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 / CVCC Infinitive /s scope. The unavailability of full-fledged low-level Amharic NLP tools makes it to create annotated corpus automatically. Moreover, there is lack of standard collection making it a major impediment to the development of Amharic IR and ls.
The derivation of m verbal roots usually b r d 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 nce 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 Amresources including a standard test collection for IR, morphologically annotated ra and compiled stopwords list; and (2) to design Amharic IR system by investig the optimal representation of documents and queries considering both root-based tem-based text representations. he rest of this paper is organized as follows. Section 2 describes Amharic language ts morphology. Section 3 discusses related work. In Section 4, we present the pro-Amharic IR system architecture along with various options to represent Amharic ments and queries.
There are ple stem templates for verbal words. However, all variants have a single root sentation 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 s from verbal roots usually / CCVC /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. 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 g 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 / CVCVC /mar k 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 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 /, it is possible to generate the following words: /n g r 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. 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 Appl. Sci. 2022, 12, x FOR PEER  REVIEW 3  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 IR 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]. Ther 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 derivati words from verbal roots usually 'she has been told'/, /n g r FOR PEER REVIEW 4 of 29 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 'he told me'/, /n g 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 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 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.
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 Amources 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. Section 5 presents the evaluation framework of the proposed adhoc retrieval system including the presentation of the resources and corpora, ental setup, and experimental results.
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 lal 3 of 29 d 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 nce 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 Amresources including a standard test collection for IR, morphologically annotated ra and compiled stopwords list; and (2) to design Amharic IR system by investig the optimal representation of documents and queries considering both root-based tem-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 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.
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/ has three prefixes ( /s 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 l preposition/, / 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.
However, all variants have representation template. The major-ity of words in the language are generat roots. Nouns, adjectives, and ad-verbs can be derived from verbal roots. Th words from verbal roots usually negation/, /t passivizer/), a perfective stem /d w l 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 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 w 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.
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. Section 3 discusses related work. In Section 4, we present the proposed 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 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 /. Thus, /s 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.