Persianp: A Persian Text Processing Toolbox

Mohseni, Mohsen; Ghofrani, Javad; Faili, Heshaam

doi:10.1007/978-3-319-75477-2_4

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this phase, depending on the characteristics of the Persian language, a series of changes are made to the file. We used PersianToolbox [17] to perform unifying, tokenizing, stemming and removing stop words tasks, and we papered and used a list of writing marks and numbers for removing writing marks and numbers. In second phase, to find keywords in Persian language texts, we first take a list of words in the text and store it in a database.…”

Section: Proposed Methodsmentioning

confidence: 99%

PAKE: a supervised approach for Persian automatic keyword extraction using statistical features

2019

View full text Add to dashboard Cite

Keywords are a collection of important words in a document that are the core topic of the discussion. This paper proposes a hybrid method for automatically extracting keywords from Persian documents and web pages. In the proposed method, firstly, based on linguistic knowledge, processing was performed at word and letter levels to optimize of the analysis. Then a new statistical features set is defined and extracted at the word level. At the final stage, keywords are determined using the SVM algorithm. Also, in this paper, due to the lack of a corpus for evaluating the methods of automatic extraction of Persian keywords, a large-scale corpus has been developed and introduced. The achieved F-measure for keywords and non-keywords are 99.89% and 99.99% respectively.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

PAKE: a supervised approach for Persian automatic keyword extraction using statistical features

2019

View full text Add to dashboard Cite

show abstract

“…Pre-processing on news documents was performed using Persianp toolkit (Mohseni et al, 2016), which includes tokenization, sentence split, and normalization. Two annotators performed the annotation task, and the agreement between them is 95% which shows the quality of the annotations.…”

Section: Data Set Creationmentioning

confidence: 99%

NSURL-2019 Task 7: Named Entity Recognition (NER) in Farsi

Taghizadeh¹,

Borhanifard²,

Melika³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

NSURL-2019 Task 7 focuses on Named Entity Recognition (NER) in Farsi. This task was chosen to compare different approaches to find phrases that specify Named Entities in Farsi texts, and to establish a standard testbed for future researches on this task in Farsi. This paper describes the process of making training and test data, a list of participating teams (6 teams), and evaluation results of their systems. The best system obtained 85.4% of F 1 score based on phrase-level evaluation on seven classes of NEs including person, organization, location, date, time, money and percent.

show abstract

Title Generation and Keyphrase Extraction from Persian Scientific Texts

Mohseni

Faili

2020

2020 25th International Computer Conference, Computer Society of Iran (CSICC)

View full text Add to dashboard Cite

Persianp: A Persian Text Processing Toolbox

Cited by 4 publications

References 9 publications

PAKE: a supervised approach for Persian automatic keyword extraction using statistical features

PAKE: a supervised approach for Persian automatic keyword extraction using statistical features

NSURL-2019 Task 7: Named Entity Recognition (NER) in Farsi

Title Generation and Keyphrase Extraction from Persian Scientific Texts

Contact Info

Product

Resources

About