Learning Fault-tolerant Speech Parsing with SCREEN

This paper describes a new approach and a system SCREEN for fault-tolerant speech parsing. SCREEEN stands for Symbolic Connectionist Robust EnterprisE for Natural language. Speech parsing describes the syntactic and semantic analysis of spontaneous spoken language. The general approach is based on incremental immediate flat analysis, learning of syntactic and semantic speech parsing, parallel integration of current hypotheses, and the consideration of various forms of speech related errors. The goal for this approach is to explore the parallel interactions between various knowledge sources for learning incremental fault-tolerant speech parsing. This approach is examined in a system SCREEN using various hybrid connectionist techniques. Hybrid connectionist techniques are examined because of their promising properties of inherent fault tolerance, learning, gradedness and parallel constraint integration. The input for SCREEN is hypotheses about recognized words of a spoken utterance potentially analyzed by a speech system, the output is hypotheses about the flat syntactic and semantic analysis of the utterance. In this paper we focus on the general approach, the overall architecture, and examples for learning flat syntactic speech parsing. Different from most other speech language architectures SCREEN emphasizes an interactive rather than an autonomous position, learning rather than encoding, flat analysis rather than in-depth analysis, and fault-tolerant processing of phonetic, syntactic and semantic knowledge.Comment: 6 pages, postscript, compressed, uuencoded to appear in Proceedings of AAAI 9

Automatic offensive language detection from Twitter data using machine learning and feature selection of metadata

The popularity of social networks has only increased in recent years. In theory, the use of social media was proposed so we could share our views online, keep in contact with loved ones or share good moments of life. However, the reality is not so perfect, so you have people sharing hate speech-related messages, or using it to bully specific individuals, for instance, or even creating robots where their only goal is to target specific situations or people. Identifying who wrote such text is not easy and there are several possible ways of doing it, such as using natural language processing or machine learning algorithms that can investigate and perform predictions using the metadata associated with it. In this work, we present an initial investigation of which are the best machine learning techniques to detect offensive language in tweets. After an analysis of the current trend in the literature about the recent text classification techniques, we have selected Linear SVM and Naive Bayes algorithms for our initial tests. For the preprocessing of data, we have used different techniques for attribute selection that will be justified in the literature section. After our experiments, we have obtained 92% of accuracy and 95% of recall to detect offensive language with Naive Bayes and 90% of accuracy and 92% of recall with Linear SVM. From our understanding, these results overcome our related literature and are a good indicative of the importance of the data description approach we have used

Learning fault-tolerant speech parsing with SCREEN

This paper describes a new approach and a system SCREEN for fault-tolerant speech parsing. Speech parsing describes the syntactic and semantic analysis of spontaneous spoken language. The general approach is based on incremental immediate flat analysis, learning of syntactic and semantic speech parsing, parallel integration of current hypotheses, and the consideration of various forms of speech related errors. The goal for this approach is to explore the parallel interactions between various knowledge sources for learning incremental fault-tolerant speech parsing. This approach is examined in a system SCREEN using various hybrid connectionist techniques. Hybrid connectionist techniques are examined because of their promising properties of inherent fault tolerance, learning, gradedness and parallel constraint integration. The input for SCREEN is hypotheses about recognized words of a spoken utterance potentially analyzed by a speech system, the output is hypotheses about the flat syntactic and semantic analysis of the utterance. In this paper we focus on the general approach, the overall architecture, and examples for learning flat syntactic speech parsing. Different from most other speech language architectures SCREEN emphasizes an interactive rather than an autonomous position, learning rather than encoding, flat analysis rather than in-depth analysis, and fault-tolerant processing of phonetic, syntactic and semantic knowledge

TEACHERS AND STUDENTS' SPEAKING POLITENESS IN LEARNING INDONESIAN AND CITIZENSHIP AND ITS IMPLICATIONS ON STUDENTS' LEARNING MOTIVATION IN CLASS VIII SMP NEGERI 8 TEBING TINGGI

This study aims to describe how the realization of language-based politeness of teachers and students in learning Indonesian and Civics in class VIII SMP Negeri 8 Tebing Tinggi, describes violations of the principle of language and language teacher pronunciation in learning Indonesian and Civics, describes the implications of language-use politeness of teachers for Bahasa Indonesia and Civics in to student learning motivation. The type of this research is qualitative and quantitative research using descriptive method. The methods used in this research are: (1) survey method, (2) conducting interview, (3) collecting recorded data, (4) conduct data reduction, (5) conduct data classification, (6) conduct testing data of research result, (7) compare data of research result, (8) checking data of research result. Data obtained from the results of document collection is analyzed by document analysis or content analysis techniques. The results of this study indicate that: (1) the realization of language-based politeness of teachers and students in learning Bahasa Indonesia and Civics, verbal and nonverbal speech forms are divided into declarative, imperative and interrogative speech form. Principles of language politeness can be categorized into six namely: generosity maxim, tact maxim, agreement maxim, acknowledgement maxim, modesty maxim, sympathy maxim, (2) violation of the principle of language-based politeness of teachers and students in learning Bahasa Indonesia and Civics due to the occurrence of the principles of language courtesy that do not comply with the principle of politeness, and inconsistency due to incomplete language usage, and (3) the implications of language-based politeness of teachers and students in learning Bahasa Indonesia and Civic to the motivation of student to learn can increase knowledge, motivation by praising God Almighty and appreciating others, motivation to be democratic, and motivation of modesty. Keywords: language politeness, learning, learning motivatio

Keberadaan Asrama Bait-Al Hikmah sebagai Ajang Penanaman Karakter Religius ( Studi Kasus di Asrama Bait-Al Hikmah Madrasah Tsanawiyah Negeri 1 Surakarta )

This research aims to: 1) describes the development of the existence of Bait-Al Hikmah boarding during this time, 2) describe the curriculum and learning that is held in Bait-Al Hikmah dormitory, 3) Describes the form of achieved by students in Bait-Al Hikmah dormitory. This type of research used is qualitative research. Research design used is phenomenological research. Data collection techniques in this study using interviews, observation, documents, and recording. Data analysis techniques using interactive data analysis model Miles and Huberman. The results of this study show that: 1) The existence of dormitory Bait-Al Hikmah is very important for santri, because in the dorm is taught worldly lessons and the afterlife that makes santri more enthusiastic in learning, 2) Curriculum in Bait-Al Hikmah dormitory ie arabic language skills, tahfidh, muta’alim talim study, tafsir study, bahstul masail, ipa guidance, arabic language guidance and arab-english language debate and learning in dormitory Bait-Al Hikmah using discussion method, question and answer and debate, 3) Achi evement is divided into two namely academic and non-academic. Academically Female students and male students dominate superior achievements such as OSN, Physics Olympiad, Mathematics Olympiad, highest UN rank of boarding children, While non-academically female students and male students dominate superior achievements such as Paskibraka competition, Scout competition, English and Arabic Speech Contest, MTQ race