Developing a persian chunker using a hybrid approach

Kiani, Soheila; Akhavan, Tara; Shamsfard, Mehrnoush

doi:10.1109/imcsit.2009.5352723

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…A phrase chunker identifies phrases like verb phrase, noun phrase etc. in a sentence but does not either specify their internal structure or their role in the main sentence [6]. Rule based and statistical methods are normally adopted for the implementation of phrase Chunker.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model For Phrase Chunking Employing Artificial Immunity System And Rule Based Methods

Bindu¹,

Idicula²

2011

IJAIA

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Natural language Understanding (NLU), an important field of Artificial Intelligence (AI) is concerned with the speech and language understanding between human and computer. Understanding language means knowing what concept a word or phrase stands for and how to link them to form meaningful sentence. Identification of phrases or phrase chunking is an important step in natural language understanding (NLU). Chunker identifies and divides sentences into syntactically correlated word groups. Question Answering (QA) systems, another important application of Artificial Intelligence (AI) mostly requires retrieval of nouns or noun phrases as answers to the questions raised by the users. Also Chunking is an important preprocessing step in full parsing. Due to high ambiguity of natural language, exact parsing of text may become very complex. This ambiguity may be partially resolved by using chunking as an intermediate step. To the best of our knowledge no known work or tag set is available for phrase chunking in Malayalam. To separate the chunks in a document it must be labeled with parts-ofspeech (POS) tags. POS Tagging is a difficult task in Malayalam as it is a complex and compounding language. In this paper we describe the application of artificial immunity system (AIS) for chunking which is implemented and obtained an accurate output with 96% precision and 93% recall. This system is tested on corpuses collected from reputed news papers and magazines. These corpuses contained documents from five different domains such as sports, health, agriculture, science and politics and each document contained sentences -simple, compound, complex-of various levels of complexity. POS tag set with 52 tags is developed for preparing the tagged corpus for Malayalam. The phrase tag set contains 20 phrase tags.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Model For Phrase Chunking Employing Artificial Immunity System And Rule Based Methods

Bindu¹,

Idicula²

2011

IJAIA

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Persianp: A Persian Text Processing Toolbox

Mohseni

Ghofrani

Faili

2018

Computational Linguistics and Intelligent Text Processing

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On the Importance of Ezafe Construction in Persian Parsing

Nourian¹,

Rasooli

Imany³

et al. 2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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Ezafe construction is an idiosyncratic phenomenon in the Persian language. It is a good indicator for phrase boundaries and dependency relations but mostly does not appear in the text. In this paper, we show that adding information about Ezafe construction can give 4.6% relative improvement in dependency parsing and 9% relative improvement in shallow parsing. For evaluation purposes, Ezafe tags are manually annotated in the Persian dependency treebank. Furthermore, to be able to conduct experiments on shallow parsing, we develop a dependency to shallow phrase structure convertor based on the Persian dependencies.

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Developing a persian chunker using a hybrid approach

Cited by 3 publications

References 6 publications

A Hybrid Model For Phrase Chunking Employing Artificial Immunity System And Rule Based Methods

A Hybrid Model For Phrase Chunking Employing Artificial Immunity System And Rule Based Methods

Persianp: A Persian Text Processing Toolbox

On the Importance of Ezafe Construction in Persian Parsing

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