Concepts and methods of complex networks can be used to analyse texts at their different complexity levels. Examples of natural language processing (NLP) tasks studied via topological analysis of networks are keyword identification, automatic extractive summarization and authorship attribution. Even though a myriad of network measurements have been applied to study the authorship attribution problem, the use of motifs for text analysis has been restricted to a few works. The goal of this paper is to apply the concept of motifs, recurrent interconnection patterns, in the authorship attribution task. The absolute frequencies of all thirteen directed motifs with three nodes were extracted from the co-occurrence networks and used as classification features. The effectiveness of these features was verified with four machine learning methods. The results show that motifs are able to distinguish the writing style of different authors. In our best scenario, 57.5 of the books were correctly classified. The chance baseline for this problem is 12.5 . In addition, we have found that function words play an important role in these recurrent patterns. Taken together, our findings suggest that motifs should be further explored in other related linguistic tasks.
Motifs were already used to analyse text @cite_17 @cite_21 . @cite_17 analysed the frequency of motifs in word adjacency networks from texts in English, French, Spanish and Japanese. Despite the differences among these four languages, the authors discovered that they present very similar interconnection patterns. One possible explanation for that is that languages possess an intrinsic structure, which divides words into categories . Therefore, words from one category (e.g. prepositions) tend to be adjacent with others from different categories (e.g. nouns or articles) @cite_31 . @cite_21 realized that some motifs, as the ones labelled as 12 and 13 in Figure below, rarely happen in real texts.
@cite_31 @cite_21 @cite_17
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We present a novel, count-based approach to obtaining inter-lingual word representations based on inverted indexing of Wikipedia. We present experiments applying these representations to 17 datasets in document classification, POS tagging, dependency parsing, and word alignment. Our approach has the advantage that it is simple, computationally efficient and almost parameter-free, and, more importantly, it enables multi-source crosslingual learning. In 14 17 cases, we improve over using state-of-the-art bilingual embeddings. Time series motifs are repeated patterns in long and noisy time series. Motifs are typically used to understand the dynamics of the source because repeated patterns with high similarity evidentially rule out the presence of noise. Recently, time series motifs have also been used for clustering, summarization, rule discovery and compression as features. For all such purposes, many high quality motifs of various lengths are desirable and thus, originates the problem of enumerating motifs for a wide range of lengths. Existing algorithms find motifs for a given length. A trivial way to enumerate motifs is to run one of the algorithms for the whole range of lengths. However, such parameter sweep is computationally infeasible for large real datasets. In this paper, we describe an exact algorithm, called MOEN, to enumerate motifs. The algorithm is an order of magnitude faster than the naive algorithm. The algorithm frees us from re-discovering the same motif at different lengths and tuning multiple data-dependent parameters. The speedup comes from using a novel bound on the similarity function across lengths and the algorithm uses only linear space unlike other motif discovery algorithms. We describe three case studies in entomology and activity recognition where MOEN enumerates several high quality motifs. The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several non-overlapping subsequences and constitutes a motif because all of the included subsequences are similar. The ability to automatically discover such motifs allows intelligent systems to form endogenously meaningful representations of their environment through unsupervised sensor analysis. In this paper, we formulate a unifying view of motif discovery as a problem of locating regions of high density in the space of all time series subsequences. Our approach is efficient (sub-quadratic in the length of the data), requires fewer user-specified parameters than previous methods, and naturally allows variable length motif occurrences and non-linear temporal warping. We evaluate the performance of our approach using four data sets from different domains including on-body inertial sensors and speech. The coinciding form and meaning similarity of cognates, e.g. ‘flamme’ (French), ‘Flamme’ (German), ‘vlam’ (Dutch), meaning ‘flame’ in English, facilitates learning of additional languages. The cross-language frequency and similarity distributions of cognates vary according to evolutionary change and language contact. We compare frequency and orthographic (O), phonetic (P), and semantic similarity of cognates, automatically identified in semi-complete lexicons of six widely spoken languages. Comparisons of P and O similarity reveal inconsistent mappings in language pairs with deep orthographies. The frequency distributions show that cognate frequency is reduced in less closely related language pairs as compared to more closely related languages (e.g., French-English vs. German-English). These frequency and similarity patterns may support a better understanding of cognate processing in natural and experimental settings. The automatically identified cognates are available in the supplementary materials, including the frequency and similarity measurements.