Number Of Short Texts Research Articles

Polysemy Needs Attention: Short-Text Topic Discovery With Global and Multi-Sense Information

The topic model has been widely applied to various research domains such as information retrieval, data mining, and so on. It can discover topics of texts in an unsupervised way. In the early years, most researches mainly focused on long texts. With the emergence of the Internet, the number of short texts is growing rapidly. Most existing schemes to solve the sparsity problems of short texts, are mainly based on data aggregation or model improvements. Among them, the Biterm Topic Model is one of the most representative models. It proposed a new way to model topics based on document-level word pairs and has shown creativity and effectiveness. However, this strategy ignores those semantically similar and rarely co-occurrent word pairs. What's more, most researches ignore the multi-sense phenomenon in natural languages. In this paper, we utilize multi-sense word vectors to extract similar word pairs from the whole corpus by considering multiple senses. Based on this idea, we introduce a novel short-text topic model, which disambiguates multiple senses of words and generates more reasonable global biterms. Experimental results on two open-source English datasets have shown superiority to state-of-the-art topic models.

Systematic framework for short text classification based on improved TWE and supervised MCFS topic merging strategy

Text classification task can help people to discover valuable information hidden in the text set. Many previous studies have achieved excellent results in traditional text classification tasks. With the development of new social media, a large number of short texts appear on the Internet. Due to the sparsity of the short text, many classification algorithms which achieve excellent results on long texts hardly achieve satisfactory results on short texts. Therefore, it is important to find a method for calculating effective vector representations of words and overcoming the feature sparseness problem. Based on the above, we carry out the work from improving the quality of word vector representation and enhancing the effect of classification. This paper proposes a systematic framework for improving short text classification performance. In our framework, we first build a topic model with Latent Dirichlet Allocation (LDA) on a universal dataset from Wikipedia and use this model to perform topic inference on short texts. Then, an improved scheme of topical word embedding (TWE) is proposed to learn the vector representations of both words and topics, which use the word in the current word-topic pair to predict the contextual words and the topic in the same word-topic pair to predict its contextual topics. In addition, the supervised Multi-Cluster Feature Selection algorithm (MCFS) is employed to execute topic selection, and we propose a topic merging strategy that is based on the MCFS. At the end of topic selection and merging, short text matrices are generated using the vector representations of both words and topics, and these matrices are fed into a convolution neural network (CNN). On an open short text classification dataset, we compared the proposed framework with various baselines, and the experimental results indicate the effectiveness of our method.

CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background

In the present big data background, how to effectively excavate useful information is the problem that big data is facing now. The purpose of this study is to construct a more effective method of mining interest preferences of users in a particular field in the context of today’s big data. We mainly use a large number of user text data from microblog to study. LDA is an effective method of text mining, but it will not play a very good role in applying LDA directly to a large number of short texts in microblog. In today’s more effective topic modeling project, short texts need to be aggregated into long texts to avoid data sparsity. However, aggregated short texts are mixed with a lot of noise, reducing the accuracy of mining the user’s interest preferences. In this paper, we propose Combining Latent Dirichlet Allocation (CLDA), a new topic model that can learn the potential topics of microblog short texts and long texts simultaneously. The data sparsity of short texts is avoided by aggregating long texts to assist in learning short texts. Short text filtering long text is reused to improve mining accuracy, making long texts and short texts effectively combined. Experimental results in a real microblog data set show that CLDA outperforms many advanced models in mining user interest, and we also confirm that CLDA also has good performance in recommending systems.

Rudolf Carnaps verschiedene Scheinproblemkonzeptionen

Abstract This paper is a review of Rudolf Carnap's changing attitudes to- wards the conceptualisation of pseudo-problems. For that purpose his early works (until 1935) are divided into four phases each of which display subtle differences with respect to the role pseudo-problems play in Carnap's epistemology and philosophical methodology. Based on a number of short texts by Carnap, an attempt is made to give provisional definitions of `pseudo-problem' and related expressions.

Le Courrier Électronique: Un Outil d'Enseignement en Milieu de Travail

This paper describes an experiment aimed at using e-mail to improve the written language skills in German and Spanish of Canadian Foreign Service officers working in various countries. Little literature exists on the use of e-mail in distance learning outside the university environment. There is, however, considerable literature documenting the very high attrition rate in distance-learning courses. The course described in this paper was developed by the Canadian Foreign Service Institute to take into account the published analyses of the reasons for high attrition rates in and for completion of distance-learning courses. Because attrition rates have been found to be much lower when course participants feel that the course will be of direct use to them in their professional life, the author and her colleagues designed the program to require the completion of authentic professional tasks. These tasks were (1) to check the correctness of a text translated into the targe t language; (2) to check the accuracy of a translated text; and (3) to write a number of short texts in the target language, such as a thank-you note, a request for information and a letter making a suggestion. The course aimed to expose the participants to formal language, in the form of official documents, technical language, through linguistic explanations and descriptions of the way the course would function, and informal language, through spontaneous interaction among the participants and with the instructor. A number of pre-course activities were required. These were (1) a pre-test to determine the participant's linguistic level; (2) a questionnaire to evaluate the participant's level of computer skills and (3) a questionnaire aimed at eliciting the participant's attitude towards the use of a computer for word-processing, the use of e-mail for interpersonal communication, the use of e-mail for learning, co-operative learning, and writing in the target language. Initial interest in the course was very high. However, the attrition rate in all groups was also very high. This latter result seems to be attributable in part to the fact that participants were paired and required to correct each other's work. It appears that participants had little interest in correcting other participants' assignments, but would have preferred an opportunity to rework their own in the light of the instructor's comments and corrections. This will be incorporated into future versions of the course. Given the initial high interest, it appears that it is worthwhile to repeat and refine the experiment.

THE "HSIN-MING" ATTRIBUTED TO NIU-T'OU FA-JUNG

In the thirtieth chapter of the celebrated Ch'an Buddhist collection “Chingte ch'uan-teng”al one finds a number of short texts of the g'tha (chia-t'a)b type2 composed by various Ch'an masters. Among these often highly abstruse “songs” (ke)c is included one called “Hsin-ming”d (Mind Inscription),3 which is attributed to Fa-junge (594–657),4 the First Patriarch of the early Ch'an Buddhist denomination commonly known as the Niu-t'ou Schoolf after the name of the mountain where the master dwelt,5 Before going on to a discussion of the text and its contents let us first take a brief look at the author and his brand of Ch'an Buddhism.