INVESTIGATING TASK PERFORMANCE OF PROBABILISTIC TOPIC MODELS: AN EMPIRICAL STUDY OF PLSA AND LDA Introduction and Problem statement: This paper deals with the task performance of PLSA(Probabilistic Latent Semantic Analysis) and LDA(Latent Dirichlet Allocation). There has been lot of work done, reporting promising performance of topic models, but none of the work has systematically investigated the task performance of topic models. As a result, some critical questions that may affect the performance of all applications of topic models are mostly unanswered, particularly • how to choose between competing models? • how multiple local maxima affect task performance? • and how to set parameters in topic models? In this paper the author address these questions by conducting a systematic investigation of two representative probabilistic topic models PLSA and LDA using three representative text mining tasks, document clustering, text categorization, and ad-hoc retrieval. Important Terms: Probabilistic Topic Models: The basic idea behind Probabilistic topic models is that documents are mixtures of topics, where a topic is represented by a multinomial distribution of words. ϕw(j) = P(w/z=j) refer to the multinomial distribution over words for topic j and θj(d)=P(z=j/d) refer to the multinomial distribution over topics for document d. The parameters ϕ and θ indicate which words are important for which topic and which topics are important for a particular document respectively. Probabilistic Latent Semantic Analysis(PLSA): PLSA was introduced by Hoffman. A document d is regarded as a sample of the following mixture model. I.e probability distribution over words w for a given document d. the word-topic distributions ϕ an... ... middle of paper ... ...been answered. The authors address these problems in this current paper empirical study of plsa and lda. A paper by Chang et al.2009 conducts user studies to quantitatively compare the semantic meaning in topics inferred by PLSA and LDA. The focus is to quantify the interpretability of topics with human effort, The author of this paper(current) study the task performance of topic models in three standard text mining applications, which can be quantified objectively using standard measures. So this work is supplementary to theirs. Previous Work: As stated above there has been lot of work done reporting promising performance of topic models like results on text categorization in the original LDA paper(Blei et al.2003). Work done by Wei and Bruce Croft(2006) shows that LDA could improve the state of art information retreival in the language modeling framework. Etc.
TOPICsearch.com - a search engine. Web.
While the Dewey decimal system contains a comprehensive index, the Library of Congress Classification system does not (Taylor 430). Each volume of the LCC schedules contains its own index and these indexes do not refer to one another. Finding subjects in the schedules can be awkward. To locate a topic, one must check through each volume index of all the different disciplines that may ...
...Page Rank Algorithm in terms of returning larger numbers of relevant pages to a given query.
It's a well known fact that humans have the ability to effeciently recognize patterns. Some people who work for Google, have highlighted the fact that backliinks, keywords, title tags and meta descritpoions are greate factors which can be utulied to sort and rank websites. However, the concept of recognizing such patterns on a massive scope is something that humans cannot easily do. Machines on the other hand, are extremly effeeint at gathering data. However, unlike humans they cannot recognize patterns as easily in terms of how certain patterns fit into the overal big picture as well as to understand what that pictures mean.
Support Vector Machine(SVM): Over the past several years, there has been a significant amount of research on support vector machines and today support vector machine applications are becoming more common in text classification. In essence, support vector machines define hyperplanes, which try to separate the values of a given target field. The hyperplanes are defined using kernel functions. The most popular kernel types are supported: linear, polynomial, radial basis and sigmoid. Support Vector Machines can be used for both, classification and regression. Several characteristics have been observed in vector space based methods for text classification [15,16], including the high dimensionality of the input space, sparsity of document vectors, linear separability in most text classification problems, and the belief that few features are relevant.
For the search strategy, a PICO was constructed and using Boolean operators, truncations, and wildcards as the following search was conducted. Student* OR Child* AND Diabetes AND manag* OR Control And school. This led to finding several articles on diabetes management in children. From the list of articles available found in the search A Collaborative Approach to Diabetes Management was chosen due to using the same model and being different enough to compare the
On-Line Newspapers and Genre Developmnet on the World Wide Web. Ludnberg, Jonas. 2001. Ulvik : s.n., 2001. Information Research System Seminar.
...x. Literature review will also help the researcher to identify the general elements, components, functions and features of the CAMA Systems.
Qualitative data such as the feedback from students about instructors can be aggregated using text mining tools to draw summarizing inferences.
Collaborative tagging is a new way to assign keywords to the internet resources by its users. It plays
The internet holds a vast amount of different topics to look up in its huge
The field of Computational Linguistics is relatively new; however, it contains several sub-areas reflecting practical applications in the field. Machine (or Automatic) Translation (MT) is one of the main components of Computational Linguistics (CL). It can be considered as an independent subject because people who work in this domain are not necessarily experts in the other domains of CL. However, what connects them is the fact that all of these subjects use computers as a tool to deal with human language. Therefore, some people call it Natural Language Processing (NLP). This paper tries to highlight MT as an essential sub-area of CL. The types and approaches of MT will be considered, and limitations discussed.
(1)In just a few short years the Internet has seen a spectacular growth in the amount of scholarly material available. Some sense of the rate of growth of electronic journals is given by the Association of Research Librarians directory of electronic journals. [1] In 1991 there were 110 journals and academic newsletters listed in their directory. This grew to 133 in 1992, 240 in 1993, 400 in 1994 (Okerson, 1994) and 700+ in 1995. There has also been remarkable growth in the number of refereed electronic journals from 74 in 1994 to 142 in 1995 (Okerson, 1995).
NLP researchers aim to gather knowledge on how human beings understand and use language so that appropriate tools and techniques can be developed to make computer systems understand and manipulate natural languages to perform the desired tasks. The foundations of NLP lie in a number of disciplines, viz. computer and information sciences, linguistics, mathematics, electrical and electronic engineering, artificial intelligence and robotics, psychology, etc. Applications of NLP include a number of fields of studies, such as machine translation, natural language text processing and summarization, user interfaces, multilingual and cross language information retrieval (CLIR), speech recognition, artificial intelligence and expert systems, and so on. One important area of application of NLP that is relatively new and has not been covered in the previous ARIST chapters on NLP has become quite prominent due to the proliferation of the world wide web and digital libraries. Several researchers have pointed out the need for appropriate research in facilitating multi- or cross-lingual information retrieval, including multilingual text processing and multilingual user interface
The vast content of the World-Wide Web is used by millions. Many users employs a search engine to begin their Web activity. The query is usually a list of keywords, and the result returned is also a list of Web pages that may or may not be relevant, typically pages that contain the keywords [4].