Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCultural Topic Modelling over Novel Wikipedia Corpora for South-Slavic Languages
النمذجة الثقافية النمذجة على رواية ويكيبيديا كورسا للغات الجنوبية-السلافية
309
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
هناك نقص في شورا عالي الجودة للغات الجنوبية السلافية. مثل هذه الشركات مفيدة لعلماء الكمبيوتر والباحثين في العلوم الاجتماعية والعلوم الإنسانية على حد سواء، مع التركيز على العديد من تطبيقات اللغات والمحتوى وتطبيقات معالجة اللغة الطبيعية. تقدم هذه الورقة سيرجال سير عمل للتعدين محتوى ويكيبيديا ومعالجته في كوربورا معالجته اللغوي، المطبق على البوسنة والكبلانية والكرواتية والكرواتية والصلبيون والرويبو الكرواتية والسلوفانية والسلوفينية. نحن نجعل سبعة كوربورا متاحة للجمهور. نعرض هذه كوربورا من خلال مقارنة محتوى ويكيبيديا الأساسي، افتراضنا أن محتوى ويكيبيداس يعكس المصالح على نطاق واسع في موضوعات مختلفة في هذه الدول البلقان. نقوم بإجراء مقارنة المحتوى باستخدام خوارزميات نمذجة الموضوع ومقارنات التوزيع المختلفة. تظهر النتائج أن جميع ويكيبيداس متشابهة موضعيا تماما، مع كلها تغطي الفن والثقافة والأدب، في حين أنها تحتوي على اختلافات في الجغرافيا والسياسة والتاريخ والعلوم.
There is a shortage of high-quality corpora for South-Slavic languages. Such corpora are useful to computer scientists and researchers in social sciences and humanities alike, focusing on numerous linguistic, content analysis, and natural language processing applications. This paper presents a workflow for mining Wikipedia content and processing it into linguistically-processed corpora, applied on the Bosnian, Bulgarian, Croatian, Macedonian, Serbian, Serbo-Croatian and Slovenian Wikipedia. We make the resulting seven corpora publicly available. We showcase these corpora by comparing the content of the underlying Wikipedias, our assumption being that the content of the Wikipedias reflects broadly the interests in various topics in these Balkan nations. We perform the content comparison by using topic modelling algorithms and various distribution comparisons. The results show that all Wikipedias are topically rather similar, with all of them covering art, culture, and literature, whereas they contain differences in geography, politics, history and science.
References used
https://aclanthology.org/
rate research
Read More
This study introduces and analyzes WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. The dataset comprises dialog triplets from the Wikipedia Talk pages, and edit
ing actions on the corresponding articles being discussed. We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor. However, they do not predict editorial behavior. On the other hand, feedback invoking evidentiality and criticism, and references to Wikipedia's community norms, is more likely to persuade the contributor to perform edits but is less likely to lead to a positive emotion. We developed baseline classifiers trained on pre-trained RoBERTa features that can predict editorial change with an F1 score of .54, as compared to an F1 score of .66 for predicting emotional change. A diagnostic analysis of persisting errors is also provided. We conclude with possible applications and recommendations for future work. The dataset is publicly available for the research community at https://github.com/kj2013/WikiTalkEdit/.
Cross-lingual summarization is a challenging task for which there are no cross-lingual scientific resources currently available. To overcome the lack of a high-quality resource, we present a new dataset for monolingual and cross-lingual summarization
considering the English-German pair. We collect high-quality, real-world cross-lingual data from Spektrum der Wissenschaft, which publishes human-written German scientific summaries of English science articles on various subjects. The generated Spektrum dataset is small; therefore, we harvest a similar dataset from the Wikipedia Science Portal to complement it. The Wikipedia dataset consists of English and German articles, which can be used for monolingual and cross-lingual summarization. Furthermore, we present a quantitative analysis of the datasets and results of empirical experiments with several existing extractive and abstractive summarization models. The results suggest the viability and usefulness of the proposed dataset for monolingual and cross-lingual summarization.
Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitat
e zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.
Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-speci
fic definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings. Herein, we propose a method for definition generation with appropriate specificity. The proposed method addresses the aforementioned problems by leveraging a pre-trained encoder-decoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-the-art method. Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.
Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level s
emantic concept. In this paper, we propose a new light-weight Self-Supervised Neural Topic Model (SNTM) that learns a rich context by learning a topic representation jointly from three co-occurring words and a document that the triple originates from. Our experimental results indicate that our proposed neural topic model, SNTM, outperforms previously existing topic models in coherence metrics as well as document clustering accuracy. Moreover, apart from the topic coherence and clustering performance, the proposed neural topic model has a number of advantages, namely, being computationally efficient and easy to train.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
