dataesr/scientific-paragraphs-categorization huggingface dataset:
Multivariate Graphs Multivariate graphs  [71]  are those in which nodes or edges carry attributes or properties. As described by Schreiber et al. [108] , there is a relationship between multivariate graphs and multilayer graphs. Some variables or attributes in a multivariate dataset often serve the purpose of distinguishing nodes and edges that belong to different layers, e. g. , the type of social network platform in our initial example. There are also multivariate visualization applications such as that of Pretorius and van Wijk  [97] , that define their graph as having discrete sets, which can be considered analogous to defining layers. However, in the majority of cases research into multivariate visualization lacks the a priori definition of a layer defined by a physical or conceptual reality related to the system being modelled.
In faceted datasets, multivariate data items are grouped in multiple orthogonal categories. Originally used as an approach to search and browse large data stores and text corpora  [21, 116] , later work extended the faceted approach to include relationship visualization  [83, 134]. Datasets can have many different facets such as spatial and temporal frames of reference, or multiple values per data item and as such can be considered multifaceted. Visualizations for multifaceted data are those which show more than one of these facets simultaneously (see Hadlak et al. [52]  for a survey of multifaceted graph visualization techniques). Hadlak et al. discuss primarily four common facets of network structure considered in network visualization, and their composition: partitions, attributes, time, and space.
This corresponds to the strongly rising topic of systems/integrative biology, where the challenge consists in understanding the interplay and the cascade of effects taking place at the different levels of the biological system at hand  [45, 77]. A prominent task for biologists analysing biological pathways consists in comparing a species-specific pathway to a reference pathway  [93] , in this specific case species type can be considered a defining aspect for a layer. Another task is to compare tissue-specific interaction networks to understand why certain tissues, e. g. , plant root tissues, synthesise certain molecules which are not found in other plant tissues. In this case tissue type is the defining aspect for a layer. Social Sciences: Datasets within Social Network analysis frequently contain multiple types of edges (e.
This can be seen in the work of Crabb et al. [27]  to understand the spread of salmonella in a large poultry farming enterprise. Different networks are generated based on contact between different types of entities. From a multilayer perspective, contact between entities can be considered an aspect, with the entity types defining the different layers. Digital Humanities: Within digital humanities fields, such as digital cultural heritage, archaeology and data journalism, many multilayer approaches  [37, 89, 101, 121 ] can be found. Digital access to source texts and natural language processing techniques such as Named-Entity Recognition and Topic Modelling allow for vast Digital Humanities datasets to be built  [89]. Co-occurrence relationships between people names, locations, organisations as well as other entities form a typical multilayer network whose analysis may reveal insightful interaction patterns.
Some of the related objectives may be to reduce the cascading of failures across these networks  [18] , but also to develop an efficient repair strategy to restore services after disaster  [111]. The precise representation of buildings to support robot control algorithms is a related domain as seen in  [115]. In this work, the graph represents a layout of the floors of the building with their interconnections. A layer is a floor containing rooms. An edge represents a direct connection between two rooms. Interlayer connections modelled connections between floors. This kind of model reduces the number of data to be analysed by a robot. The vast number of instances of complex datasets produced across all these examples demands a visual approach to help understand it, and that approach will often be multilayer network visualization.
Categorisation The categorisation of the most important features of multilayer network visualization that are to be considered for each paper is built in a manner consistent with Munzner's nested visualization design process model  [92] : Tasks and Analysis. Multilayer systems that address new problems and domains may expose tasks that do not fit in existing task taxonomies, such as  [82, 98]. New analytics have been developed for multilayer networks, and new visualizations have been developed as a result, e. g. ,  [30]. Data Definition. This aspect of the review looks at the nomenclature used for the dataset e. g. , multiplex, heterogeneous, which aspects are used to define layers across the data, as well as the structure of the data. Visualization Approach. We analyse and categorise the various visualization approaches described, identifying novel approaches and novel applications of existing approaches e.
1). Other systems and approaches infer multilayer structure by aggregating data from multiple sources, whether databases  [74]  or a collection of ego networks (as in  [36] ) and/or personal data  [62]. Interestingly enough, some systems do not directly target the visualization of multilayer networks, but use multiplex and/or hypergraph representations to build query graphs or summarise query response  [109, 119]. Obviously, MuxViz  [30]  relies on the exact definition and implementation (see Section 2. 1) introduced by  [73] , which is also the case of authors mentioning explicit use of the MuxViz framework  [42]. Elementary layers originating from aspects of the network, such as time or node/edge type, are quite similar to the facets described in  [52]. Detangler  [102]  relies on an explicit encoding of layers, with a goal to allow an easy exploration of inter-layer correlation (see Section 4.
Visualization Approaches From a multilayer network perspective, previous work in network visualization techniques may be classified based on their awareness of the notion of a layer. When this is the case, layers are visually encoded using any appropriate Gestalt principle in a way that structures the spatial representation; they are also manipulated as visual objects in their own right as detailed in Section 4. 1. This is why this section is organised based on the type of visual encoding used to show layers explicitly. This survey also documents and reflects on the widespread use of weaker visual cues (in the sense of Mackinlay's ranking of perceptual tasks  [88] ) to encode layer information, such as node or link colour. GraphDice  [9]  (multi facet query) (R5, R12).
Edges are only drawn between adjacent layers, and within layer edges are shown in a separate visualization. Node order on axes can be set based on edge attributes or connectivity to other layers. They use their approach to analyse the NSF funding dataset. Examples of tasks they carry out include determining whether some NSF program manager award funding to some PIs more often than others on a 3-layer networking containing program managers, projects, and PIs. This is an instance of Task category A where the focus is on paths traversing all layers. The list view of Jigsaw  [118]  provides an overview of entities grouped by type, with edges being drawn between connected entities in adjacent lists. One of the main utilities of this system is to relate different types of named entities (people, geographic locations, organisations) mentioned in the same documents.
2D, 2. 5D and 3D Node-Link Representations Across the various papers we surveyed, node-link layouts cropped up frequently. The MuxViz toolkit  [30] , from the domain of complex systems, utilises standard node-link visualizations. They are also used in other domains that depend on complex systems theory  [8, 29, 42]. A widespread visual design consists in encoding layer information using node colour or shape, as depicted in  [41, 74, 90, 133]. Colour coding of edges is also used in  [30, 35]. This design choice relies on the law of similarity of Gestalt theory (colour similarity in this case). This design is often adopted when the multivariate nature of the network is the driving motivation of the visual design. For instance, Figure  6  represents flows of maritime traffic using colour to encode different modes of shipping (or layers).
For example the SetCola constraintbased layout of Hoffswell et al. [59]  allows users to apply layout constraints to sets of nodes, which might easily correspond to layers. Such a layout approach supports analysing cross layer connectivity (Task category A) as well as layer comparison (Task category D2). The examples covered by the authors include a food web networks and a network modelling a biological cell, and both of these datasets can be considered to have multilayer characteristics. Inspired by the multi-level nature of some prob-lem areas e. g. , biological networks, the 2. 5D approach materialises layers as 2D translucent parallel planes in a three dimensional layout, similar in spirit to Figure  2. This visual design relies on the law of uniform connectedness of Gestalt theory. It separates links lying within layer from those between layers providing a more natural support for path related tasks (Task categories A and B) than traditional 2D node-link layouts, but 3D navigation is required to allow the user to change his perspective on the data and resolve visual occlusion problems.
In their example use case, using an academic publication dataset, the heterogeneity of the data is derived from node types, and edges only exist between certain node types. There is no formal layer definition and the abstraction used to provide the hierarchical focus and context abstraction is applied across all data types and does not fully consider the heterogeneity of the data. Such abstractions could be adapted to be applied on a per layer basis. This could be very useful in multilayer systems, particularly for comparison of complex layers.
Combined with edge bundling, as done by Holten  [61]  with Hierarchical Edge Bundling, it could also be used to examine structural and categorical attribute difference between layers simultaneously (Task categories B and D2). It is also possible to consider categorical attribute data as a network layer in and of itself. For example, in the application OntoVis  [112] , Shen et al. use a node ontology graph to query a large heterogeneous social network dataset. The node ontology graph reflects the disparity of the attribute (how well distributed it is across nodes), and its edges display frequency of links between the entities. It acts both as a visualization of aggregated categorical data, and a layer by which the dataset can be better interacted Figure  9 : A screen shot from Detangler  [102]  showing how nodes (left panel) relate to layers (right panel).
Within domains external to those related to information visualization there is less demand for performing a thorough evaluation of systems or techniques, so authors may just demonstrate the techniques with a suitable dataset, e. g. ,  [28]. The MuxViz application displays layers in a 2D planes in a 3D visualization (a. k. a 2. 5D), in one of the many types of visualization it offers. However, as mentioned earlier, no empirical evaluation has ever shown such use of 3D graph visualization to be beneficial, with the exception of when viewed with stereo and/or motion or depth cues  [50, 126].
While the process is analytical, it still results in a layering that reflects the reality of the system being modelled. Projection is another means by which layers can be created. Bipartite systems can be analysed by projecting on a node type  [78]. For example, a bipartite authorpaper network, where researchers are connected to papers that they authored. A projection on the paper node type results in a co-authorship network of researchers, where two researchers are connected if they ever authored a paper. Such an operation may be adapted to a multilayer user case. Degree of interest (DOI) functions suggest nodes for inclusion based on what the user has already characterised as interesting. This approach has already been used by the Refinery application and may also be applied to datasets that are explicitly multilayer  [79].
The approach used by  [28]  is not developed much beyond the original edge bundling algorithm of Holten and van Wijk, while the bundling of  [15]  focuses on edge routing in the case where the nodes and edges of all layers are presented in a single node-link diagram. Within this report we have intentionally avoided focusing on more complex data modelling approaches such as hyper-graphs. However, it is worth noting that in many applications, especially in the domain of biology, the datasets are explicitly modelled as hypergraphs, e. g. , the Systems Biology Graph Notation (SBGN)  [81]  that is often used to describe biological pathways. Representing hyper-edges in a multilayer context (particularly if endpoints belong to discrete layers), is an interesting open challenge. Some multilayer datasets also contain a temporal aspect, e.
2 The data that we present in this paper have been collected in the village of Mahon. The data are based on a corpus resulting from long-term fieldwork by one of the authors between 2007 and 2012. It is composed of 1500 recordings of a group of native speakers  (Traoré 2016). Traoré has obtained a phonological inventory which comes from the analysis of 621 basic roots (loanwords, compounds and derivations were excluded).
Tier hierarchization As we have shown in §2. 2, Tagba data do not fit the tone association convention perfectly. Let us look first at a "canonic" case of tonal behavior. (22) Ewe vowel coalescence  (Clements & Ford 1979)  mēkpɔ́ ètú > mēkp ɔtú In (  22 ), to resolve the hiatus /ɔè/, Ewe chooses to delete the second vowel /è/, but not its underlying L tone. This kind of phenomenon has been considered to be strong evidence since the inception of the autosegmental theories that the tonal tier is independent from the segmental tier. However, if we compare the Tagba data in (20a), we observe the opposite phenomenon: the tone drops with vowel deletion. If Tagba behaves differently, the organization of its tonal and segmental tiers should then be reconsidered.
Many of the world's languages, such as Niger-Congo, Finno-Ugric and Altaic languages have vowel harmony  (Maddieson, 2013). They may differ in the harmonizing phonological features (e. g. vowel height, backness, roundedness etc. ), the harmonizing levels (i. e. , lexicon, morphology or both), as well as in the extent to which vowel harmony is regular. In some languages, vowel harmony is pervasive, having relatively few exceptions: a large percentage of the lexicon (i. e. of word stems) shows harmony, and most affixes have different harmonic allomorphs. In Turkish, for example, the majority of the lexicon is harmonic. Indeed, 85% of a very large corpus, comprising 500,000,000 word tokens, of adult-directed written text was found to be harmonic  (Avar, 2015). Similarly, 89% of the 200 most frequent word types in infant-directed speech in the Turkish subcorpora of the CHILDES database was shown to be harmonic  (Ketrez, 2014).
Corpus Analyses We conducted a quantitative analysis of the frequency of harmonic vs. nonharmonic words in child-directed Hungarian using the Hungarian subcorpora  (MacWhinney, 1974; Réger, 2004)  of the CHILDES database  (MacWhinney, 2000) , considering word stems and morphologically complex word forms both separately and jointly, in order to quantify the extent to which the lexicon and the morphology are regular in terms of harmony in Hungarian. Only one previous study investigated the frequency of harmonic forms in infant-directed Hungarian  (Ketrez, 2014). This study found that among the 200 most frequent word types in the CHILDES subcorpora of Turkish, Hungarian, Farsi and Polish, 89. 47%, 77. 40%, 32. 4% and 54. 6%, respectively, were harmonic (i. e. , contained only front or only back vowels).
Hence, harmonic words appear to be more frequent in Hungarian than in the two non-harmonic languages, Farsi and Polish. At the same time, they appear to be less frequent than in Turkish. However, these results remain suggestive since that previous study only took into account type frequency, not token frequency, and only considered a small number of very frequent words. Furthermore, it pooled roots and morphologically complex words together, thus it did not evaluate the prevalence of harmony in the lexicon and in morphology separately. Here, we will, therefore, investigate the Hungarian subcorpora of the CHILDES database taking into account all word types. We will also run our counts over word tokens. We will further distinguish between monomorphemic stems and morphologically complex words.
Corpus We used the two Hungarian subcorpora  (MacWhinney, 1974; Réger, 2004)  of the CHILDES database  (MacWhinney, 2000) , as pre-processed in Gervain and Guevara Erra (  2012 ) and available at http://childes. psy. cmu. edu/derived/. The MacWhinney subcorpus  (MacWhinney, 1974; 1975)  contains orthographic transcripts of recordings of six Hungarian children (age: 1;5-2;10, 3 boys and 3 girls) in their usual kindergarten environment over a period of 10 months. The Réger corpus  (Réger, 2004)  contains orthographic transcripts of the recordings of a Hungarian boy between the ages 1;11 and 2;11 in his family environment. From these subcorpora, we extracted all the adult utterances, except those of one adult, who is not a native speaker of Hungarian. This way, a corpus of 15,231 utterances, 54,881 word tokens and 8,234 word types was compiled.
Method Participants. Thirty full-term, monolingual infants from Hungarian-speaking families were tested in Budapest: Sixteen 10-month-olds (mean age = 10 months 10 days; range: 10 months 1 day -30 days; 8 girls, 8 boys) and fourteen 13-month-olds (mean age = 13 months 23 days; range: 13 months 13 days -31 days; 9 girls, 5 boys). The data of eighteen additional 10-month-olds and nine 13-month-olds were not included in the analyses due to completing fewer than 12 of the 16 trials (n = 14), fussiness/crying (n = 9), too many short looks (n = 2), technical error (n = 1) or parental interference (n = 1). Stimuli. The stimuli were composed of vowel-consonant-vowel (V1CV2) sequences. This sequence structure was chosen in order to have only one nonadjacent relation within each item (as done in the study on the PA bias, Gonzalez Gomez & Nazzi, 2015; but differently to stimuli of van  Kampen et al.
Consonants were chosen in order to obtain balanced adjacent regularities between the anterior-posterior and posterior-anterior lists and the posterior-posterior and anterior-anterior lists for the entire V1CV2 sequence as well as for the V1C and CV2 sequences according to the segmented Hungarian subcorpora of the CHILDES database  (MacWhinney, 2000; Gervain & Guevara Erra, 2012). A set of paired t-tests confirmed that the mean frequency of the adjacent regularities between the harmonic (i. e. , anterior-anterior and posterior-posterior) and nonharmonic (i. e. , anterior-posterior and posterior-anterior) stimuli was not statistically significant for V1CV2, V1C or CV2 (see Table  3 ). All of these items were pseudo-words that follow the phonotactic regularities of Hungarian. The stimuli were recorded in a sound-attenuated booth by a Hungarian female native speaker.
Each stimulus was played to completion or stopped after the infant failed to maintain the gaze for 2 consecutive seconds. If the infant looked away from the target in any direction for less than 2s and then looked back again, the trial continued, but the time spent looking away (when the experimenter released the buttons) was automatically subtracted from the orientation time by the program. Thus, the maximum looking time for a given trial was the duration of the entire speech sample. Infants' looking behavior was monitored online by a single experimenter and coded offline by a different experimenter using the video recording made during the experiment. Only the offline data was used for the analyses. Each session began with two musical trials, one presented on each side, to give infants an opportunity to practice one head-turn to each side before the actual test phase began.
Results and Discussion Mean looking times to the harmonic and non-harmonic lists were calculated for each infant. The data for the 10-month-olds (MH = 8. 79 s, SD = 3. 14 s; MNH = 8. 68 s, SD = 1. 86), and for the 13-month-olds (MH = 7. 64 s, SD = 2. 14 s; MNH = 9. 95 s, SD = 2. 66 s), are presented in Figure  2. A 2-way ANOVA with the between-subject factor of Age (10 months versus 13 months) and the within-subject factor of Harmonicity (H versus NH) was conducted. The effect of Harmonicity was significant (F(1, 28) = 5. 26, p =. 03, ηp 2 =. 16), infants having longer orientation times to the NH than to the H lists. The effect of Age was not significant (F(1, 28) =. 006, p =. 94). However, the interaction between Age and Harmonicity was significant (F(1, 28) = 6. 36, p =. 018, ηp 2 =. 19), indicating that the effect of Harmonicity changed with age.
Method Participants. Fourteen monolingual, full-term 13-month-old infants from Frenchspeaking families were tested in Paris (mean age = 13 months 20 days; range: 13 months 15 days -29 days; 8 girls, 6 boys). The data of five additional infants were not included in the analyses due to fussiness/crying. Stimuli, Procedure and Apparatus. The stimuli and the procedure were identical to the ones in Experiment 1. The apparatus had some minor differences. First, the lights were arranged on three separate panels, one central and one on each side of the infant. Second, if a trial lasted less than 1. 5s, the software automatically repeated the trial and only the looking time of this second trial was used. Third, infants had to accumulate 15 seconds of listening time to musical trials before the testing phase began.
Note that the Hungarian pseudowords, which had been created for Experiment 1 to have balanced adjacent regularities in Hungarian, also had balanced adjacent regularities in French, as attested by analyses conducted on the French database Lexique 3  (New, Pallier, Ferrand, & Matos, 2001). A set of paired t-tests confirmed that the mean frequency of the adjacent regularities between the harmonic (i. e. , anterior-anterior and posterior-posterior) and non-harmonic (i. e. , anterior-posterior and posterior-anterior) stimuli was not statistically significant for V1CV2, V1C or CV2 (see Table  5 ). All of these items were pseudo-words that also follow the phonotactic regularities of French. Table  5. Mean frequency (and SDs) associated to the stimuli used in Experiment 2, for the words themselves (V1CV2), and their constituting diphone sequences (V1C and CV2) according to Lexique 3  (New, Pallier, Ferrand, & Matos, 2001).
Acknowledgements This study was conducted with the support of  ANR-13-BSH2-0004  to TN,  ANR-16-FRAL-0007  to TN and SS,  Labex EFL  ( ANR-10-LABX-0083 ) to TN and JG, as well as a  Fyssen Foundation   Startup grant , the  Emergence(s) program  grant of the  City of Paris  and the  ANR-15-CE37-0009-01  grant to JG. Special thanks to the infants and their parents for their kindness and cooperation. We wish to thank  Gergely Csibra ,  Agnes Volein  and the whole Cognitive Developmental Center of the  Central European University, Budapest, Hungary , for their invaluable help and support with testing the Hungarian infants.