Book
Proceedings of the 2015 Workshop on Topic Models: Post-Processing and Applications
The main objective of the workshop is to bring together researchers who are interested in applications of topic models and improving their output. Our goal is to create a broad platform for researchers to share ideas that could improve the usability and interpretation of topic models. We hope this workshop will promote topic model applications in other research areas, making their use more effective.
We received a total of 12 paper submissions from around the world, which were subject to a rigorous peer review process by an international program committee. A total of 8 papers were accepted for publication and appear in the workshop proceedings. In keeping with the theme of "Post-processing and Applications", there was strong representation of topic model applications among the accepted papers.
We propose a new method of feature extraction for regression problems with text data that transforms the sparse texts to dense features using regularized topic models. We also discuss the problem of topic model initialization, and propose a new approach based on Naive Bayes. This approach is compared to many others, and it achieves a quality comparable to vector space models using as little as ten topics. It also outperforms other methods for feature generation based on topic modeling, such as PLSA and Supervised LDA.
The paper proposes a substantial classification of collocates (pairs of words that tend to cooccur) along with heuristics that can help to attibute a word pair to a proper type automatically.
The best studied type is frequent phrases, which includes idioms, lexicographic collocations, and syntactic selection. Pairs of this type are known to occur at a short distance and can be singled out by choosing a narrow window for collecting cooccurrence data.
The next most salient type is topically related pairs. These can be identified by considering word frequencies in individual documents, as in the wellknown distributional topic models.
The third type is pairs that occur in repeated text fragments such as popular quotes of standard legal formulae. The characteristic feature of these is that the fragment contains several aligned words that are repeated in the same sequence. Such pairs are normally filtered out for most practical purposes, but filtering is usually applied only to exact repeats; we propose a method of capturing inexact repetition.
Hypothetically one could also expect to find a forth type, collocate pairs linked by an intrinsic semantic relation or a long-distance syntactic relation; such a link would guarantee co-occurrence at a certain relatively restricted range of distances, a range narrower than in case of a purely topical connection, but not so narrow as in repeats. However we do not find many cases of this sort in the preliminary empirical study.
The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study.
ARTM advantages: ARTM is much simpler that Bayesian Inference ARTM focuses on formalizing task-specific requirements ARTM simplifies the multi-objective PTMs learning ARTM reduces barriers to entry into PTMs research field ARTM encourages the development of regularization library ARTM restrictions: Choosing a regularization path is a new open issue for PTMs
The paper describes the results of an experimental study of statistical topic models applied to the task of automatic single-word term extraction. The English part of the Europarl parallel corpus from the socio-political domain and the Russian articles taken from online banking magazines were used as target text collections. The experiments demonstrate that topic information can improve the quality of single-word term extraction regardless of the subject area and the target language.
Abstract. The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traffic is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the final node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a finite-dimensional system of differential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of differential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.