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Showing papers on "Probabilistic latent semantic analysis published in 2013"


Posted Content
TL;DR: Probabilistic Latent Semantic Analysis (PLSA) as mentioned in this paper is a statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text and in related areas.
Abstract: Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.

2,233 citations


Proceedings ArticleDOI
27 Oct 2013
TL;DR: A series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them are developed.
Abstract: Latent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails In this study we strive to develop a series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data To make our models applicable to large-scale Web search applications, we also use a technique called word hashing, which is shown to effectively scale up our semantic models to handle large vocabularies which are common in such tasks The new models are evaluated on a Web document ranking task using a real-world data set Results show that our best model significantly outperforms other latent semantic models, which were considered state-of-the-art in the performance prior to the work presented in this paper

1,935 citations


Proceedings ArticleDOI
01 Jan 2013
TL;DR: This paper proposes a novel NMFbased multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views and designs a novel and effective normalization strategy inspired by the connection between NMF and PLSA.
Abstract: Many real-world datasets are comprised of different representations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multiview clustering algorithms have been developed to cluster multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In this paper, we propose a novel NMFbased multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views. The key idea is to formulate a joint matrix factorization process with the constraint that pushes clustering solution of each view towards a common consensus instead of fixing it directly. The main challenge is how to keep clustering solutions across different views meaningful and comparable. To tackle this challenge, we design a novel and effective normalization strategy inspired by the connection between NMF and PLSA. Experimental results on synthetic and several real datasets demonstrate the effectiveness of our approach.

754 citations


Journal ArticleDOI
TL;DR: A new method for automatic landslide detection from remote-sensing imagery using a scene classification method based on the bag-of-visual-words (BoVW) representation in combination with the unsupervised probabilistic latent semantic analysis (pLSA) model and the k-nearest neighbour (k-NN) classifier is presented.
Abstract: Landslide detection from extensive remote-sensing imagery is an important preliminary work for landslide mapping, landslide inventories, and landslide hazard assessment. Aimed at development of an automatic procedure for landslide detection, a new method for automatic landslide detection from remote-sensing imagery is presented in this study. We achieved this objective using a scene classification method based on the bag-of-visual-words BoVW representation in combination with the unsupervised probabilistic latent semantic analysis pLSA model and the k -nearest neighbour k -NN classifier. Given a remote-sensing image, we divided it into equal-sized square sub-images and then described each sub-image as a BoVW representation. The pLSA model was applied to sub-images by using the BoVW representation to discover the object classes depicted in the sub-images, and then a k -NN classifier was used to classify the sub-images into landslide areas and non-landslide areas based on object distribution. We investigated the performance and applicability of the method using remote-sensing imagery from the Ili area. The results show that the method is robust and can produce good performance without the acquisition of three-dimensional 3D topography. We anticipate that these results will be helpful in landslide inventory mapping and landslide hazard assessment in landslide-stricken areas.

211 citations


Book
27 Apr 2013
TL;DR: This chapter discusses Latent Trait Theory, a model for Latent Class Theory, and its applications to Criterion-Referenced Testing.
Abstract: and Overview.- I Latent Trait Theory.- 1 Measurement Models for Ordered Response Categories.- 2 Testing a Latent Trait Model.- 3 Latent Trait Models with Indicators of Mixed Measurement Level.- II Latent Class Theory.- 4 New Developments in Latent Class Theory.- 5 Log-Linear Modeling, Latent Class Analysis, or Correspondence Analysis: Which Method Should Be Used for the Analysis of Categorical Data?.- 6 A Latent Class Covariate Model with Applications to Criterion-Referenced Testing.- III Comparative Views of Latent Traits and Latent Classes.- 7 Test Theory with Qualitative and Quantitative Latent Variables.- 8 Latent Class Models for Measuring.- Chaffer 9 Comparison of Latent Structure Models.- IV Application Studies.- 10 Latent Variable Techniques for Measuring Development.- 11 Item Bias and Test Multidimensionality.- 12 On a Rasch-Model-Based Test for Noncomputerized Adaptive Testing.- 13 Systematizing the Item Content in Test Design.

190 citations


Journal ArticleDOI
01 Aug 2013
TL;DR: A new way of using normative databases as a way of processing text with a dimensional model and compare it with different categorical approaches is introduced and shows that the categorical model using NMF and the dimensional model tend to perform best.
Abstract: Text often expresses the writer's emotional state or evokes emotions in the reader. The nature of emotional phenomena like reading and writing can be interpreted in different ways and represented with different computational models. Affective computing (AC) researchers often use a categorical model in which text data are associated with emotional labels. We introduce a new way of using normative databases as a way of processing text with a dimensional model and compare it with different categorical approaches. The approach is evaluated using four data sets of texts reflecting different emotional phenomena. An emotional thesaurus and a bag-of-words model are used to generate vectors for each pseudo-document, then for the categorical models three dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Non-negative Matrix Factorization (NMF). For the dimensional model a normative database is used to produce three-dimensional vectors (valence, arousal, dominance) for each pseudo-document. This three-dimensional model can be used to generate psychologically driven visualizations. Both models can be used for affect detection based on distances amongst categories and pseudo-documents. Experiments show that the categorical model using NMF and the dimensional model tend to perform best.

179 citations


Proceedings Article
01 Oct 2013
TL;DR: A new discriminative term-weighting metric called TF-KLD is designed, which outperforms TF-IDF and it is shown that using the latent representation from matrix factorization as features in a classification algorithm substantially improves accuracy.
Abstract: Matrix and tensor factorization have been applied to a number of semantic relatedness tasks, including paraphrase identification. The key idea is that similarity in the latent space implies semantic relatedness. We describe three ways in which labeled data can improve the accuracy of these approaches on paraphrase classification. First, we design a new discriminative term-weighting metric called TF-KLD, which outperforms TF-IDF. Next, we show that using the latent representation from matrix factorization as features in a classification algorithm substantially improves accuracy. Finally, we combine latent features with fine-grained n-gram overlap features, yielding performance that is 3% more accurate than the prior state-of-the-art.

158 citations


Journal ArticleDOI
TL;DR: In this paper, a Variational Bayesian algorithm for the Latent Position Cluster Model (LPSM) is proposed, where sampling based MCMC is replaced by an optimization that requires many orders of magnitude fewer iterations to converge.

105 citations


Journal ArticleDOI
TL;DR: This review of the latent tree model, a particular type of probabilistic graphical models, deserves attention because its simple structure allows simple and efficient inference, while its latent variables capture complex relationships.
Abstract: In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summarize key ideas underlying the model. Second we explain how it can be efficiently learned from data. Third we illustrate its use within three types of applications: latent structure discovery, multidimensional clustering, and probabilistic inference. Finally, we conclude and give promising directions for future researches in this field.

94 citations


Proceedings Article
16 Jun 2013
TL;DR: A new probabilistic model for capturing this phenomenon, which is called latent feature propagation, in social networks, is introduced and its capability for inferring such latent structure in varying types of social network datasets is demonstrated.
Abstract: Current Bayesian models for dynamic social network data have focused on modelling the influence of evolving unobserved structure on observed social interactions. However, an understanding of how observed social relationships from the past affect future unobserved structure in the network has been neglected. In this paper, we introduce a new probabilistic model for capturing this phenomenon, which we call latent feature propagation, in social networks. We demonstrate our model's capability for inferring such latent structure in varying types of social network datasets, and experimental studies show this structure achieves higher predictive performance on link prediction and forecasting tasks.

73 citations


Journal ArticleDOI
TL;DR: This paper describes research that seeks to supersede human inductive learning and reasoning in high-level scene understanding and content extraction using the latent Dirichlet allocation model into a finite mixture over an underlying set of topics.
Abstract: This paper describes research that seeks to supersede human inductive learning and reasoning in high-level scene understanding and content extraction. Searching for relevant knowledge with a semantic meaning consists mostly in visual human inspection of the data, regardless of the application. The method presented in this paper is an innovation in the field of information retrieval. It aims to discover latent semantic classes containing pairs of objects characterized by a certain spatial positioning. A hierarchical structure is recommended for the image content. This approach is based on a method initially developed for topics discovery in text, applied this time to invariant descriptors of image region or objects configurations. First, invariant spatial signatures are computed for pairs of objects, based on a measure of their interaction, as attributes for describing spatial arrangements inside the scene. Spatial visual words are then defined through a simple classification, extracting new patterns of similar object configurations. Further, the scene is modeled according to these new patterns (spatial visual words) using the latent Dirichlet allocation model into a finite mixture over an underlying set of topics. In the end, some statistics are done to achieve a better understanding of the spatial distributions inside the discovered semantic classes.

Proceedings Article
01 Oct 2013
TL;DR: It is demonstrated that by integrating multiple relations from both homogeneous and heterogeneous information sources, MRLSA achieves state-of-the-art performance on existing benchmark datasets for two relations, antonymy and is-a.
Abstract: We present Multi-Relational Latent Semantic Analysis (MRLSA) which generalizes Latent Semantic Analysis (LSA). MRLSA provides an elegant approach to combining multiple relations between words by constructing a 3-way tensor. Similar to LSA, a lowrank approximation of the tensor is derived using a tensor decomposition. Each word in the vocabulary is thus represented by a vector in the latent semantic space and each relation is captured by a latent square matrix. The degree of two words having a specific relation can then be measured through simple linear algebraic operations. We demonstrate that by integrating multiple relations from both homogeneous and heterogeneous information sources, MRLSA achieves stateof-the-art performance on existing benchmark datasets for two relations, antonymy and is-a.

Proceedings ArticleDOI
Jason Weston1, Ron Weiss1, Hector Yee1
12 Oct 2013
TL;DR: This work proposes to model the user with a richer set of functions, specifically via a set of latent vectors, where each vector captures one of the user's latent interests or tastes, and describes a simple, general and efficient algorithm for learning such a model.
Abstract: Classical matrix factorization approaches to collaborative filtering learn a latent vector for each user and each item, and recommendations are scored via the similarity between two such vectors, which are of the same dimension In this work, we are motivated by the intuition that a user is a much more complicated entity than any single item, and cannot be well described by the same representation Hence, the variety of a user's interests could be better captured by a more complex representation We propose to model the user with a richer set of functions, specifically via a set of latent vectors, where each vector captures one of the user's latent interests or tastes The overall recommendation model is then nonlinear where the matching score between a user and a given item is the maximum matching score over each of the user's latent interests with respect to the item's latent representation We describe a simple, general and efficient algorithm for learning such a model, and apply it to large scale, real-world datasets from YouTube and Google Music, where our approach outperforms existing techniques

Journal ArticleDOI
TL;DR: A Markov Random Field for real-valued image modeling that has two sets of latent variables that gate the interactions between all pairs of pixels, while the second set determines the mean intensities of each pixel is described.
Abstract: This paper describes a Markov Random Field for real-valued image modeling that has two sets of latent variables. One set is used to gate the interactions between all pairs of pixels, while the second set determines the mean intensities of each pixel. This is a powerful model with a conditional distribution over the input that is Gaussian, with both mean and covariance determined by the configuration of latent variables, which is unlike previous models that were restricted to using Gaussians with either a fixed mean or a diagonal covariance matrix. Thanks to the increased flexibility, this gated MRF can generate more realistic samples after training on an unconstrained distribution of high-resolution natural images. Furthermore, the latent variables of the model can be inferred efficiently and can be used as very effective descriptors in recognition tasks. Both generation and discrimination drastically improve as layers of binary latent variables are added to the model, yielding a hierarchical model called a Deep Belief Network.

Book ChapterDOI
24 Mar 2013
TL;DR: The investigation on semantic similarity measures at word- and sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach are presented.
Abstract: We present in this paper the results of our investigation on semantic similarity measures at word- and sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach. The focus is on similarity measures based on Latent Dirichlet Allocation, due to its novelty aspects, while the Latent Semantic Analysis measures are used for comparison purposes. We explore two types of measures based on Latent Dirichlet Allocation: measures based on distances between probability distribution that can be applied directly to larger texts such as sentences and a word-to-word similarity measure that is then expanded to work at sentence-level. We present results using paraphrase identification data in the Microsoft Research Paraphrase corpus.

Journal ArticleDOI
TL;DR: This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word-time count matrices, and proposes a general method that favors the recovery of sparse distributions by adding simple regularization constraints on the searched distributions to the data likelihood optimization criteria.
Abstract: This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns called motifs from documents given as word $$\times $$ time count matrices (e.g., videos). In this model, documents are represented as a mixture of sequential activity patterns (our motifs) where the mixing weights are defined by the motif starting time occurrences. The novelties are multi fold. First, unlike previous approaches where topics modeled only the co-occurrence of words at a given time instant, our motifs model the co-occurrence and temporal order in which the words occur within a temporal window. Second, unlike traditional Dynamic Bayesian networks (DBN), our model accounts for the important case where activities occur concurrently in the video (but not necessarily in synchrony), i.e., the advent of activity motifs can overlap. The learning of the motifs in these difficult situations is made possible thanks to the introduction of latent variables representing the activity starting times, enabling us to implicitly align the occurrences of the same pattern during the joint inference of the motifs and their starting times. As a third novelty, we propose a general method that favors the recovery of sparse distributions, a highly desirable property in many topic model applications, by adding simple regularization constraints on the searched distributions to the data likelihood optimization criteria. We substantiate our claims with experiments on synthetic data to demonstrate the algorithm behavior, and on four video datasets with significant variations in their activity content obtained from static cameras. We observe that using low-level motion features from videos, our algorithm is able to capture sequential patterns that implicitly represent typical trajectories of scene objects.

Journal ArticleDOI
TL;DR: A state-of-the-art system for finding objects in cluttered images based on deformable models that represent objects using local part templates and geometric constraints on the locations of parts is described.
Abstract: We describe a state-of-the-art system for finding objects in cluttered images. Our system is based on deformable models that represent objects using local part templates and geometric constraints on the locations of parts. We reduce object detection to classification with latent variables. The latent variables introduce invariances that make it possible to detect objects with highly variable appearance. We use a generalization of support vector machines to incorporate latent information during training. This has led to a general framework for discriminative training of classifiers with latent variables. Discriminative training benefits from large training datasets. In practice we use an iterative algorithm that alternates between estimating latent values for positive examples and solving a large convex optimization problem. Practical optimization of this large convex problem can be done using active set techniques for adaptive subsampling of the training data.

Journal ArticleDOI
TL;DR: This article introduces Regularized Latent Semantic Indexing (RLSI)---including a batch version and an online version, referred to as batch and online RLSI, respectively---to scale up topic modeling and proposes adopting ℓ1 norm on topics andℓ2 norm on document representations to create a model with compact and readable topics and which is useful for retrieval.
Abstract: Topic modeling provides a powerful way to analyze the content of a collection of documents. It has become a popular tool in many research areas, such as text mining, information retrieval, natural language processing, and other related fields. In real-world applications, however, the usefulness of topic modeling is limited due to scalability issues. Scaling to larger document collections via parallelization is an active area of research, but most solutions require drastic steps, such as vastly reducing input vocabulary. In this article we introduce Regularized Latent Semantic Indexing (RLSI)---including a batch version and an online version, referred to as batch RLSI and online RLSI, respectively---to scale up topic modeling. Batch RLSI and online RLSI are as effective as existing topic modeling techniques and can scale to larger datasets without reducing input vocabulary. Moreover, online RLSI can be applied to stream data and can capture the dynamic evolution of topics. Both versions of RLSI formalize topic modeling as a problem of minimizing a quadratic loss function regularized by e1 and/or e2 norm. This formulation allows the learning process to be decomposed into multiple suboptimization problems which can be optimized in parallel, for example, via MapReduce. We particularly propose adopting e1 norm on topics and e2 norm on document representations to create a model with compact and readable topics and which is useful for retrieval. In learning, batch RLSI processes all the documents in the collection as a whole, while online RLSI processes the documents in the collection one by one. We also prove the convergence of the learning of online RLSI. Relevance ranking experiments on three TREC datasets show that batch RLSI and online RLSI perform better than LSI, PLSI, LDA, and NMF, and the improvements are sometimes statistically significant. Experiments on a Web dataset containing about 1.6 million documents and 7 million terms, demonstrate a similar boost in performance.

Journal ArticleDOI
TL;DR: This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents.
Abstract: This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

Proceedings Article
01 Jun 2013
TL;DR: A novel method is presented for the computation of compositionality within a distributional framework that is modeled as a multi-way interaction between latent factors, which are automatically constructed from corpus data.
Abstract: In this paper, we present a novel method for the computation of compositionality within a distributional framework. The key idea is that compositionality is modeled as a multi-way interaction between latent factors, which are automatically constructed from corpus data. We use our method to model the composition of subject verb object triples. The method consists of two steps. First, we compute a latent factor model for nouns from standard co-occurrence data. Next, the latent factors are used to induce a latent model of three-way subject verb object interactions. Our model has been evaluated on a similarity task for transitive phrases, in which it exceeds the state of the art.

Journal ArticleDOI
TL;DR: In this scheme,domain ontology is first constructed using the graph-based approach to automating construction of domain ontology GRAONTO proposed by the group, and query semantic extension and retrieval are then adopted for semantic-based knowledge retrieval.

Journal ArticleDOI
TL;DR: A novel technique called Affective Circumplex Transformation (ACT) is proposed for representing the moods of music tracks in an interpretable and robust fashion based on semantic computing of social tags and research in emotion modeling, and its performance is robust against a low number of track-level mood tags.
Abstract: Social tags inherent in online music services such as Last.fm provide a rich source of information on musical moods. The abundance of social tags makes this data highly beneficial for developing techniques to manage and retrieve mood information, and enables study of the relationships between music content and mood representations with data substantially larger than that available for conventional emotion research. However, no systematic assessment has been done on the accuracy of social tags and derived semantic models at capturing mood information in music. We propose a novel technique called Affective Circumplex Transformation (ACT) for representing the moods of music tracks in an interpretable and robust fashion based on semantic computing of social tags and research in emotion modeling. We validate the technique by predicting listener ratings of moods in music tracks, and compare the results to prediction with the Vector Space Model (VSM), Singular Value Decomposition (SVD), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). The results show that ACT consistently outperforms the baseline techniques, and its performance is robust against a low number of track-level mood tags. The results give validity and analytical insights for harnessing millions of music tracks and associated mood data available through social tags in application development.

Patent
Alfio Gliozzo1
14 Aug 2013
TL;DR: In this article, a system and method that improves obtaining similarity measure between concepts based on Latent Semantic Analysis by taking onto account graph structure derived from the knowledge base by using a vector propagation algorithm, in the context domain, such as a medical domain.
Abstract: A system and method that improves obtaining similarity measure between concepts based on Latent Semantic Analysis by taking onto account graph structure derived from the knowledge bases by using a vector propagation algorithm, in the context domain, such as a medical domain. Concepts contained in a corpus of documents are expressed in a graph wherein each node is a concept and edges between node express relation between concepts weighted by the number of semantic relations determined from the corpus. A vector of neighbors is created and assigned to each concept, thereby providing an improved similarity measure between documents, i.e., corpus and query against corpus.

Proceedings Article
16 Jun 2013
TL;DR: This work derives an optimization problem for estimating (alternative) parameters of a latent tree graphical model, allowing it to represent the marginal probability table of the observed variables in a compact and robust way, and derives a novel decomposition based on this framework.
Abstract: We approach the problem of estimating the parameters of a latent tree graphical model from a hierarchical tensor decomposition point of view. In this new view, the marginal probability table of the observed variables is treated as a tensor, and we show that: (i) the latent variables induce low rank structures in various matricizations of the tensor; (ii) this collection of low rank matricizations induces a hierarchical low rank decomposition of the tensor. We further derive an optimization problem for estimating (alternative) parameters of a latent tree graphical model, allowing us to represent the marginal probability table of the observed variables in a compact and robust way. The optimization problem aims to find the best hierarchical low rank approximation of a tensor in Frobenius norm. For correctly specified latent tree graphical models, we show that a global optimum of the optimization problem can be obtained via a recursive decomposition algorithm. This algorithm recovers previous spectral algorithms for hidden Markov models (Hsu et al., 2009; Foster et al., 2012) and latent tree graphical models (Parikh et al., 2011; Song et al., 2011) as special cases, elucidating the global objective these algorithms are optimizing. For misspecified latent tree graphical models, we derive a novel decomposition based on our framework, and provide approximation guarantee and computational complexity analysis. In both synthetic and real world data, this new estimator significantly improves over the state-of-the-art.

Journal ArticleDOI
TL;DR: It is demonstrated that this algorithm produces sensible semantic maps from two existing bodies of data and concludes that universal semantic graph structure can be automatically approximated from cross-language semantic data.
Abstract: Semantic maps are a means of representing universal structure underlying semantic variation. However, no algorithm has existed for inferring a graphbased semantic map from cross-language data. Here, we note that this open problem is formally identical to the known problem of inferring a social network from disease outbreaks. From this identity it follows that semantic map inference is computationally intractable, but that an efficient approximation algorithm for it exists. We demonstrate that this algorithm produces sensible semantic maps from two existing bodies of data. We conclude that universal semantic graph structure can be automatically approximated from crosslanguage semantic data.

Proceedings Article
27 Jun 2013
TL;DR: This paper proposes a new approach to mine features from the object-oriented source code of a set of software variants based on Formal Concept Analysis and Latent Semantic Indexing, and applies it on ArgoUML and Mobile Media case studies.
Abstract: Companies often develop a set of software variants that share some features and differ in other ones to meet specific requirements. To exploit existing software variants and build a software product line (SPL), a feature model of this SPL must be built as a first step. To do so, it is necessary to mine optional and mandatory features from the source code of the software variants. Thus, we propose, in this paper, a new approach to mine features from the object-oriented source code of a set of software variants based on Formal Concept Analysis and Latent Semantic Indexing. To validate our approach, we applied it on ArgoUML and Mobile Media case studies. The results of this evaluation validate the relevance and the performance of our proposal as most of the features were correctly identified.

Posted Content
TL;DR: Zhang et al. as mentioned in this paper proposed a zero-shot learning method for semantic utterance classification, which learns a classifier for problems where none of the semantic categories $Y$ are present in the training set.
Abstract: We propose a novel zero-shot learning method for semantic utterance classification (SUC). It learns a classifier $f: X \to Y$ for problems where none of the semantic categories $Y$ are present in the training set. The framework uncovers the link between categories and utterances using a semantic space. We show that this semantic space can be learned by deep neural networks trained on large amounts of search engine query log data. More precisely, we propose a novel method that can learn discriminative semantic features without supervision. It uses the zero-shot learning framework to guide the learning of the semantic features. We demonstrate the effectiveness of the zero-shot semantic learning algorithm on the SUC dataset collected by (Tur, 2012). Furthermore, we achieve state-of-the-art results by combining the semantic features with a supervised method.

Book ChapterDOI
11 Sep 2013
TL;DR: This paper explores several probabilistic topic models: Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation and Correlated Topic Model to extract latent factors from web service descriptions and introduces a new approach for discovering web services using latent factors.
Abstract: In Information Retrieval the Probabilistic Topic Models were originally developed and utilized for topic extraction and document modeling. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. These extracted latent factors are then used to group the services into clusters. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering web services using latent factors. In our experiment, we compared the accuracy of the three probabilistic clustering algorithms (PLSA, LDA and CTM) with that of a classical clustering algorithm. We evaluated also our service discovery approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn). The results show that both approaches based on CTM and LDA perform better than other search methods.

Journal ArticleDOI
TL;DR: In this article, a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits.
Abstract: Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits. Parameters of the new class of models can be estimated using the Bayesian approach with Markov chain Monte Carlo methods. Through a series of simulations, the authors demonstrated that the parameters in the new class of models can be well recovered with the computer software WinBUGS, and the joint estimation approach was more efficient than multistaged or consecutive approaches. Two empirical examples of achievement and personality assessments were given to demonstrate applications and implications of the new models.

Book ChapterDOI
29 Jul 2013
TL;DR: Experiments with several semantic similarity measures based on the unsupervised method Latent Dirichlet Allocation indicate that the method based on word representations as topic vectors outperforms methods based on distributions over topics and words.
Abstract: We present in this paper experiments with several semantic similarity measures based on the unsupervised method Latent Dirichlet Allocation. For comparison purposes, we also report experimental results using an algebraic method, Latent Semantic Analysis. The proposed semantic similarity methods were evaluated using one dataset that includes student answers from conversational intelligent tutoring systems and a standard paraphrase dataset, the Microsoft Research Paraphrase corpus. Results indicate that the method based on word representations as topic vectors outperforms methods based on distributions over topics and words. The proposed evaluation methods can also be regarded as an extrinsic method for evaluating topic coherence or selecting the number of topics in LDA models, i.e. a task-based evaluation of topic coherence and selection of number of topics in LDA.