State of Art for Semantic Analysis of Natural Language Processing

Maulud, Dastan Hussen; Zeebaree, Subhi R. M.; Jacksi, Karwan; Sadeeq, Mohammed Mohammed; Sharif, Karzan Hussein

doi:10.48161/qaj.v1n2a40

Cited by 60 publications

(13 citation statements)

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“…There are critical voices on gender issues for voice assistants in a UNESCO report 6 and in the media 7 . There is also an initiative for a genderless voice to end gender bias in AI 8 .…”

Section: Discussion and Future Workmentioning

confidence: 99%

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How Should Voice Assistants Deal With Users' Emotions?

Ma¹,

Drewes²,

Butz³

2022

Preprint

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There is a growing body of research in HCI on detecting the users' emotions. Once it is possible to detect users' emotions reliably, the next question is how an emotion-aware interface should react to the detected emotion. In a first step, we tried to find out how humans deal with negative emotions of an avatar. The hope behind this approach was to identify human strategies, which we can then mimic in an emotion-aware voice assistant. We present a user study in which participants were confronted with an angry, sad, or frightened avatar. Their task was to make the avatar happy by talking to it. We recorded the voice signal and analyzed it. The results show that users predominantly reacted with neutral emotion. However, we also found gender differences, which opens a range of questions.CCS Concepts: • Human-centered computing → Natural language interfaces.

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“…There are critical voices on gender issues for voice assistants in a UNESCO report 6 and in the media 7 . There is also an initiative for a genderless voice to end gender bias in AI 8 .…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…A good overview on this approach is provided by Schuller [15]. Another possibility is the semantic analysis of natural language [6], which analyzes what was said.…”

Section: Related Workmentioning

confidence: 99%

How Should Voice Assistants Deal With Users' Emotions?

Ma¹,

Drewes²,

Butz³

2022

Preprint

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“…Sehingga mengakibatkan jika kalimat tersebut dibacakan maka sulit untuk dipahami maksud dari kalimat tersebut. Maulud et al, (2021). Klasifikasi teks semantik melibatkan bahasa alami yang jauh di bawah kedudukan dengan melampaui informasi sintaksis belaka dan menangkap sifat semantik yang kompleks seperti sinonim, polisemi, dan negasi Mondal et al, (2020).…”

Section: Kalimat Berstruktur Tidak Bakuunclassified

Pemetaan Gagasan Pada Teks Laporan Dan Analisis Kesalahan Berbahasa SMP Kelas 7

Markhamah,

Darmasari

2024

aksara

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This study examines the analysis of language errors in report texts for 7th grade junior high school (SMP) students. The background of this research is what forms of language errors often occur in student report texts and why students still experience language errors when writing scientific papers such as reports. The purpose of this study was to examine the analysis of students' academic writing errors. own terminology. The data collection technique of this research is direct observation and analysis of the report text obtained. the results of the discussion found several language errors in the fields of phonology, morphology, semantics, syntax. language errors that occur in the report text of grade 7 junior high school students are caused by the low writing and reading skills of students. As well as the low mastery of student vocabulary so that there are several forms of writing that are wrong and not in accordance with the Big Indonesian Dictionary (KBBI). Conclusions can be written in paragraph form. The level of accuracy and neatness of students in writing report texts is also very influential on the final results of their writing later.

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“…In TF-based LSA, the cells are filled with the term frequency (TF i ) of terms in the complaint statement (C j ) as in (3).…”

Section: ) Term-sentence Matrixmentioning

confidence: 99%

“…Measuring the semantic similarity between sentences is a long-standing problem in the Natural Language Processing (NLP) field [1], [2]. With the growth of text data over time, NLP became essential to be worthy of attention for Artificial Intelligence (AI) experts [3], [4]. Semantic similarity is used for several fields in NLP like information retrieval, text summarization, plagiarism detection, question answering, document clustering, text classification, machine translation, and others [5], [6].…”

Section: Introductionmentioning

confidence: 99%