Web Blog Content Curation Using Fuzzy-Related Capsule Network-Based Auto Encoder

The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method.

Accounting for Sentence Position and Legal Domain Sentence Embedding in Learning to Classify Case Sentences

In this paper, we treat sentence annotation as a classification task. We employ sequence-to-sequence models to take sentence position information into account in identifying case law sentences as issues, conclusions, or reasons. We also compare the legal domain specific sentence embedding with other general purpose sentence embeddings to gauge the effect of legal domain knowledge, captured during pre-training, on text classification. We deployed the models on both summaries and full-text decisions. We found that the sentence position information is especially useful for full-text sentence classification. We also verified that legal domain specific sentence embeddings perform better, and that meta-sentence embedding can further enhance performance when sentence position information is included.

صعوبات الطلبة في تعلم النحو وعواملها

Nahwu discusses the methods for arranging sentences correctly in Arabic by paying attention to sighot, sentence position, and the line's state at the end of the sentence. Thus, students have various kinds of difficulties in understanding Arabic grammar, especially in Nahwu. This study uses the literature review method, which describes some of the problems from the results of research that have been conducted by other researchers related to students' difficulties with Arabic grammar, especially in Nahwu. Therefore, the researcher describes the two difficulties students face in learning Nahwu, namely the form of difficulties and the factors causing the difficulties that students face in learning Nahwu. The results of this study; (1) the forms of difficulty faced by students in learning Nahwu, a) linguistic aspects, most students have difficulty reading and providing lines in Arabic text, b) students have difficulty translating, (2) the factors that cause difficulty faced by students in learning Nahwu, a) lack of interest and motivation of students in learning Nahwu, b) using books, methods, and media.

The production-comprehension link in prosodic development and individual differences

Central to the debate on the production-comprehension link in prosodic development is the acquisition of focus-to-prosody mapping. To elucidate the nature of the production-comprehension link and shed first light on individual differences in the prosodic domain, the present study investigated developmental changes in production and comprehension of the focus-to-prosody mapping in Dutch-speaking children (age range: 4;8 ~ 7;5, N = 71) longitudinally. It was found that children’s comprehension is predictive of their production only if their comprehension is already adult-like but their production isn’t. Notably, individual differences in the production-comprehension link change with both sentence-position and age, challenging the assertion in the literature that individual differences are stable across development and domains in first language acquisition.

Argument and case linking of Polish Experiencers

This article aims to test whether the Theta System of Reinhart (1996, 2000, 2001, 2002) can account for the puzzles associated with psychological verbs in Polish. The first puzzle, called argument linking, relates to the mapping of the Experiencer onto a subject or an object position. The second puzzle, referred to as case linking, concerns the fact that Experiencers may be marked for different cases in the same sentence position. The analysis of Object Experiencer (OE)/Subject Experiencer (SE) alternations in Polish carried out in this article demonstrates that the predictions of the Theta System about Experiencer argument linking are borne out by the Polish data. SE alternants of eventive OE verbs in Polish show unergative properties, which directly follows from the mechanisms of the Theta System. However, the Theta System faces problems when confronted with dyadic OE verbs with dative Experiencers. The model predicts that dative Experiencers are merged internally, as a part of an unaccusative structure. This prediction is untenable for Polish, because dative Experiencers of dyadic predicates show some characteristics of external arguments, and hence must merge externally. Consequently, the conclusion drawn is that the Theta System can provide solutions to some, but not all, of the argument and case-linking puzzles associated with Polish Experiencers.

Peringkasan Multi Dokumen Berita Dengan Pemilihan Kalimat Utama Berbasis Algoritma Cluster Importance Dengan Mempertimbangkan Posisi Kalimat

AbstrakPeringkasan teks merupakan salah satu cara untuk mengurangi suatu dimensi dokumen yang besar untuk mendapatkan informasi penting dari dokumen tersebut. Berita adalah salah satu informasi yang biasanya dalam satu topik memiliki beberapa sub topik. Untuk dapat mengambil informasi penting dari satu topik secara cepat, peringkasan multi dokumen berita dapat menjadi solusi. Namun, peringkasan multi dokumen dapat menimbulkan redundansi. Oleh sebab itu, penelitian ini menerapkan algoritma cluster importance dengan mempertimbangkan posisi kalimat untuk mengatasi redundansi tersebut. Penelitian ini menggunakan 30 topik berita berbahasa Indonesia, dimana tiap topiknya terdiri dari 5 sub topik berita. Dari 30 topik berita yang diuji menggunakan Rouge-1, dimana terdapat 2 topik berita yang memiliki nilai Rouge-1 berbeda antara yang menggunakan algoritma cluster importance ditambah posisi kalimat dengan yang hanya menggunakan algoritma cluster. Namun dari 2 topik berita tersebut, nilai Rouge-1 yang menggunakan cluster importance ditambah posisi kalimat memiliki nilai yang lebih besar daripada yang hanya menggunakan cluster importance. Penggunaan posisi kalimat memiliki pengaruh terhadap urutan bobot kalimat pada setiap topiknya, namun hanya 2 topik berita yang berpengaruh terhadap hasil ringkasan. Abstract Text summarization is one of way to reduce large document dimension to get an important point of information. News is one of information which usually has some sub topics from one topic. In order to get the main information from one topic as fast as possible, multi document summarization is the solution. But sometimes it can create redundancy. So in this study, we applied cluster importance algorithm by considering sentence position to overcome the redundancy.This study used 30 topics of Indonesian news, where each topic consists 5 news sub topics. From 30 news topics where it has tested using Rouge-1, there are 2 news topics that have a Rouge-1 score differ between which used cluster importance algorithm by considering sentence position and which only used cluster importance. But, those 2 news topics which used cluster importance by considering sentence position have a greater score of Rouge-1 than which only used cluster importance. The use of sentence position had an effect on the order of sentence weights on each topic, but there was only 2 news topics that affect the outcome of the summary.

NLP: Text Summarization By Frequency And Sentence Position Methods

In today's fast-growing online information age we have an abundance of text, especially on the web. New information is constantly being generated. Often due to time constraints we are not able to consume all the data available. It is therefore essential to be able to summarize the text so that it becomes easier to ingest, while maintaining the essence and understandability of the information. The summarizer basically uses the combinations of term frequency and sentence position methods with language specific lexicons in order to identify the most important sentence for extractive summary. We aim to design an algorithm that can summarize a document by their performance both objectively and subjectively in Afan Oromo Language. The performance of the summarizers was measured based on subjective as well as objective evaluation methods. The techniques used in this paper are term frequency and sentence position methods with language specific lexicons to assign weights to the sentences to be extracted for the summary