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Detection of semantic errors from simple bangla sentences
The document discusses the detection of semantic errors in simple Bangla sentences, focusing on subject-verb and object-verb relationships. It outlines the objectives, methodology, and implementation details, including the creation of a corpus with various verb and subject categories. The research highlights the importance of semantic correctness in machine learning and intelligent agent development and suggests future work in extending the methodology to complex sentences.
Detection of semantic errors from simple bangla sentences
- 1. Detection of SemanticErrors from Simple Bangla Sentences Authors K. M. Azharul Hasan, Muhammad Hozaifa, Sanjoy Dutta Presented By Muhammad Hozaifa Department of Computer Science and Engineering (CSE) Khulna University of Engineering & Technology Khulna 9203, Bangladesh. 1ICCIT 2014
- 2. CONTENTS Problem Statement Objectives &Goal Semantic Error Detection from Simple Bangla Sentences Implementations Experimental results Conclusion and future works 2ICCIT 2014
- 3. PROBLEM STATEMENT • Semanticsexpresses a range of ideas, from the language to the highly technical. Used in ordinary language for denoting a problem of understanding • In linguistics, semantics deals with the study of meaning, changes in meaning, and the principles that govern the relationship between sentences or words and their meanings • The research on semantic correctness checking is very important for the purpose of machine learning, opinion mining and intelligent agent development for human computer interaction. 3ICCIT 2014
- 4. PROBLEM STATEMENT 4 • ForSimple sentence Semantically Wrong Semantically Correct ICCIT 2014
- 5. CONTENTS Problem Statement Related Works Objectives& Goal Semantic Error Detection from Simple Bangla Sentences Implementations Experimental results Conclusion and future works 5ICCIT 2014
- 6. RELATED WORKS • SomaPaul describes an analysis of the unification two verb Bangla sentences (V1 and V2) by using semantic principle of compounding Based on HPSG structure. • Beth Levin discovers the behavior of a verb, particularly with respect to the expression and interpretation of its arguments, • Levin classifies over 3,000 English verbs according to shared meaning and behavior into different categories. ICCIT 2014 6
- 7. CONTENTS Problem Statement Objectives &Goal Semantic Error Detection from Simple Bangla Sentences Implementations Experimental results Conclusion and future works 7ICCIT 2014
- 8. OBJECTIVES & GOAL •Check the semantic correctness. • Establishment of Subject Verb (SV) relationship and Object Verb (OV) relationship to check the semantic correctness of both SV and OV relation. 8ICCIT 2014
- 9. CONTENTS Problem Statement Objectives &Goal Semantic Error Detection from Simple Bangla Sentences Implementations Experimental results Conclusion and future works 9ICCIT 2014
- 10. SEMANTIC ERROR DETECTIONFROM SIMPLE BANGLA SENTENCES • For this work, we follow Simple Sentence format of Bangla text. Subject +Object + Verb (SOV) • In the SOV form, the relation of the verb with subject and object is of two fold: 1. Whether the verb with the subject has a well formed structure with semantic compatibility (SV relation) 2. Whether the Object and Verb (OV relation) has semantic compatibility. • For explanation, we consider some semantically wrong examples, 1. (SV error) 2. (OV error) 10ICCIT 2014
- 11. SEMANTIC ERROR DETECTIONFROM SIMPLE BANGLA SENTENCES • SV relationship also depends on person and tense. 11ICCIT 2014
- 12. SEMANTIC ERROR DETECTIONFROM SIMPLE BANGLA SENTENCES • Steps towards solution: 1. Noun Categorization I. Animal Approval Category II. Clipping-Animal approval category 2. Verb Categorization I. Main-Verb Category II. Verb-with-Inflection Category 3. Relationship Validation and Acceptance Checking 4. Take decision whether the sentence is correct or wrong. 12ICCIT 2014
- 13. SEMANTIC ERROR DETECTIONFROM SIMPLE BANGLA SENTENCES 13ICCIT 2014
- 14. SEMANTIC ERROR DETECTIONFROM SIMPLE BANGLA SENTENCES Verb 1 2 (VF) 2 (F) 3 (F) 2/3 (P) bôla ami boli tui bolish tumi bôlo she bôle apni bôlen বলা আমি বমল তুই বমলস তুমি বললা সস বলল আপমি বললি khola ami khuli tui khulish tumi kholo she khole apni kholen স ালা আমি ুমল তুই ুমলস তুমি স াললা সস স ালল আপমি স াললি khêla ami kheli tui khelish tumi khêlo she khêle apni khêlen স লা আমি স মল তুই স মলস তুমি স ললা সস স লল আপমি স ললি chena ami chini tui chinish tumi cheno she chene apni chenen সেিা আমি মেমি তুই মেমিস তুমি সেলিা সস সেলি আপমি সেলিি jana ami jani tui janish tumi jano she jane apni janen জািা আমি জামি তুই জামিস তুমি জালিা সস জালি আপমি জালিি hôoa ami hoi tui hosh tumi hôo she hôe apni hôn হওযা আমি হই তুই সহাস তুমি হও সস হয আপমি হি dhoa ami dhui tui dhush tumi dhoo she dhoe apni dhon স াযা আমি ুই তুই ুস তুমি স াও সস স ায আপমি স াি khaoa ami khai tui khash tumi khao she khae apni khan াওযা আমি াই তুই াস তুমি াও সস ায আপমি াি deoa ami dii tui dish tumi dao she dêe apni dên সেওযা আমি তলে মেই তুই তালে মেস তুমি োও সস সেয আপমি সেি 14 Table : Forms of Simple present tense ICCIT 2014
- 15. CONTENTS Problem Statement Objectives &Goal Semantic Error Detection from Simple Bangla Sentences Implementations Experimental results Conclusion and future works 15ICCIT 2014
- 16. IMPLEMENTATIONS • Developed acorpus of our own in which we used almost 10000 words in different categories. • In this corpus, we tried to sort all types of verbs of Bangla language. • Verb Categories : 57 types • Subject Categories : 50 types • So in our corpus, we tried to cover almost 2850 categories to find out the relations. ICCIT 2014 16
- 17. IMPLEMENTATIONS • Strategy ofSemantic analysis (a) Take sentences and check formation with word (b) Check the subject (c) Check the verb (d) Check the relationship between Subject and Verb with proper Object. (e) Take a decision for semantic formulation 17ICCIT 2014
- 18. IMPLEMENTATIONS • (a) Takesentences and check for SOV standard sentence. • (b) Check the subject 1. Animal Approval Category 2. Clipping-Animal approval category Subject category - িানুষ: “তুমি সতাি সতািা আপমি আপিা তাো” ;(Second person) Sample sentence: “সতািো বামিলত আলসা” Clipped process: সতাি – ো (Clipped) => সতাি => িানুষ 18ICCIT 2014
- 19. IMPLEMENTATIONS • (c) Checkthe verb 1. Main-Verb Category 2. Verb-with-Inflection Category 19 Sentence Verb Decision আমরা খেলাধুলা করছি কর+ ছি(Verb-inflection) Verb : কর Category : কর মছিষগুললা জাবড় কাটলি কাট+ খি (Verb Inflection) Verb : কাট Category : কাট ICCIT 2014
- 20. IMPLEMENTATIONS • (d) Checkthe relationship between Subject and Verb with proper Object. 20 Sentence : Subject : Verb : Object :( ^ ^ True Sentence : Subject : Verb : Object : ( ^ ^ False ICCIT 2014
- 21. IMPLEMENTATIONS • (e) Takea decision for semantic formulation 1.If the SVO relationship is okay. 2. If the S-V relationship is okay but SVO relationship is not okay. 3. If the S-V relationship is not okay. 21ICCIT 2014
- 22. CONTENTS Problem Statement Objectives &Goal Semantic Error Detection from Simple Bangla Sentences Implementations Experimental results Conclusion and future works 22ICCIT 2014
- 23. EXPERIMENTAL RESULTS ফাতিমার ঈদে ছুটিহদেদছ। সে সবড়াদি যাদব। কক্সবাজার অদেক েুন্দর জােগা। সে কক্সবাজার যাদব। তমিু কক্সবাজাদর থাদক। সে চাকরী কদর। িারা বাদে চড়দি পছন্দ কদর। েমুদে গাড়ী চদ। SOV standard = 05 & Others = 03 Error = 01 Correct=04 Not detected=03 23ICCIT 2014
- 24. CONTENTS Problem Statement Objectives &Goal Semantic Error Detection from Simple Bangla Sentences Implementations Experimental results Conclusion and future works 24ICCIT 2014
- 25. CONCLUSION AND FUTURE WORKS •In this paper, we have worked on to detect the semantic analysis for SIMPLE Bangla text. • Important and Necessary to complete the VALIDATION TABLE and OBJECT VERB RELATION TABLE for all the verbs and nouns of Bangla language. • There are a LOT OF SCOPES in this field. We only worked on SIMPLE SENTENCE (SOV). • Although there are also COMPLEX and COMPOUND structures in Bangla grammar. This PROPOSED ALGORITHM can easily be extended for complex and compound sentences for semantic error detection. 25ICCIT 2014
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