Метод учёта структуры биграмм в тематических моделях
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.
We propose a generalized probabilistic topic model of text corpora which can incorporate heuristics of Bayesian regularization, sampling, frequent parameters update, and robustness in any combinations. Well- known models PLSA, LDA, CVB0, SWB, and many others can be considered as special cases of the proposed broad family of models. We propose the robust PLSA model and show that it is more sparse and performs better that regularized models like LDA.
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 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.
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.
In this paper we introduce a generalized learning algorithm for probabilistic topic models (PTM). Many known and new algorithms for PLSA, LDA, and SWB models can be obtained as its special cases by choosing a subset of the following “options”: regularization, sampling, update frequency, sparsing and robustness. We show that a robust topic model, which distinguishes specific, background and topic terms, doesn’t need Dirichlet regularization and provides controllably sparse solution.