A topic detection approach based on a probabilistic framework is proposed to realize topic adaptation of speech recognition systems for long speech archives such as meetings. Since topics in such speech are not clearly defined unlike news stories, we adopt a probabilistic representation of topics based on probabilistic latent semantic analysis (PLSA). A topical sub-space is constructed by PLSA,...

The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-based collaborative filtering algorithms, such as knearest neighbor, have been shown to be quite vulnerable to such attacks. In this paper, we examine the robustness of model-based recommendation algorithms in the face...

The volume of documents in the digital repositories numbers in thousands and is increasing constantly, in such a scenario it becomes a very important issue to organize and retrieve these documents in a way that relates to the human mind. In this paper, we present a novel approach to classify the documents in a digital repository and find the semantically significant keywords related to those do...

Emotion words have been well used as the most obvious choice as feature in the task of textual emotion recognition and automatic emotion lexicon construction. In this work, we explore features for recognizing word emotion. Based on RenCECps (an annotated emotion corpus) and MaxEnt (Maximum entropy) model, several contextual features and their combination have been experimented. Then PLSA (proba...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...

Latent Semantic Analysis (LSA) is a mathematical technique that is used to capture the semantic structure of documents based on correlations among textual elements within them. Summaries of documents contain words that actually contribute towards the concepts of documents. In the present work, summaries are used in LSA along with supplementary information such as document category and domain in...

The method based on local features has an advantage that the important local motion feature is represented as bag-of-features, but lacks the location information. Additionally, in order to employ an approach based on bag-of-features, language models represented by pLSA and LDA (Latent Dirichlet Allocation) have to be applied to. These are unsupervised learning, but they require the number of la...