Sripada, Gowri Somayajulu

Thomas, Kavita

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Atlas.txt: Linking Geo-referenced Data to Text for NLGKavita E. Thomas and Somayajulu SripadaDepartment of Computing ScienceUniversity of AberdeenAberdeen, UK{tkavita,ssripada}@csd.abdn.ac.ukAbstractGeo-referenced data which are often communicated viamaps are inaccessible to the visually impaired popula-tion. We summarise existing approaches to improvingaccessibility of geo-referenced data and present the At-las.txt project which aims to produce textual summariesof such data which can be read out via a screenreader.We outline issues involved in generating descriptions ofgeo-referenced data and present initial work on contentdetermination based on knowledge acquisition from bothparallel corpus analysis and input from visually impairedpeople. In our corpus analysis we build an ontologycontaining abstract representations of expert-written sen-tences which we associate with macros containing se-quences of data analysis methods. This helps us to iden-tify which data analysis methods need to be applied togenerate text from data.1 IntroductionCurrently there is a plethora of geo-referenced sta-tistical data such as census data publicly accessibleon the World Wide Web. Much of this data is pro-vided by governmental organisations like NationalStatistics which provides the UK census 2001 dataonline1. Such organisations are legally required tomake this data accessible to user groups with visualimpairments. However the majority of this data iscurrently displayed in the form of chloropleth mapswhich shade regions according to the mean value ofa given variable in that region, as is typically exem-plified in Figure 12 and is described by the expertauthored text:“Of Scotland’s Census Day population of 5,062,011,1At http://www.statistics.gov.uk/census2001/census2001.asp.2Scotland’s Census Results Online, www.scrol.gov.uk.Figure 1: A map showing geo-referenced dataclose to 2 million people live in large urban areas”.Such maps of geo-referenced data aid sighted usersto quickly comprehend spatial patterns and trends,e.g., that the Scottish population is largely concen-trated among the urban areas in Figure 1. How-ever, visually impaired users have to listen to eachof the individual frequency values corresponding tothe census area units before comprehending the spa-tial distribution of population. The Atlas.txt projectaims to improve access to such geo-referenced datausing NLG technology by determining the salientfeatures which best explain the data and communi-cating this relevant information as text summarieslike the one above, which can then be read out viaa screen-reader. The project initially addresses pre-dominantly census data, but aims to develop tech-niques that can be applied on other geo-referenceddata such as land-use data.2 Related WorkSystems which generate descriptions of numeri-cal data are not uncommon, e.g., the FOG system(Goldberg et al, 1994), TREND (Boyd, 1997) andMULTI-METEO (Coch, 1998) all generate textualsummaries of numerical weather data. (Ferres etal, 2006) generates textual descriptions of informa-tion in graphs and enables user querying. SumTime(Sripada et al, 2003) summarises time-series data.While there is no prior work on generating textualdescriptions of geo-referenced data, there have beenstudies on describing spatial data in the context ofroute directions ((Geldof, 2003); (Marciniak andStrube, 2004)), scene descriptions (Novak, 1986),geometric descriptions (Mitkov, 1990) and spatialdescriptions (Ebert et al, 1996). RoadSafe3, whichgenerates weather forecast texts for road mainte-nance based on spatio-temporal weather predictiondata, also explores similar issues. However, in theAtlas.txt project our main focus is on describinggeo-spatial data to visually impaired users.Other projects address the issue of accessibilitywith other user interaction paradigms in mind, forexample haptic interfaces and non-speech sonic in-terfaces. Of these, the most closely related are soni-fication (non-speech audio) approaches to data com-munication. (Stockman, 2004) discusses issues in-volved with sonification of spreadsheet data. Ofparticular interest is the iSonic (Zhao, 2005) dataexploration tool which uses sonification techniquesto aid interactive exploration of geo-referenced datadisplayed as chloropleth maps. Such approachescan be seen as complementary to our approach.3 Knowledge AcquisitionIn order to elicit end-user requirements and coor-dinate evaluation subjects, we interviewed a visu-ally impaired volunteer at the Grampian Society forthe Blind4 (GSB) who is responsible for helpingblind users with computer accessibility. He demon-strated how he and other blind users he knows cur-rently explore data; if the data is in tabular form,they use spreadsheet applications which computedescriptive statistics such as the mean and rangeof a column (row) and use these statistics to grad-ually build a mental picture of the distribution ofthe data. Analysing geo-referenced data addition-ally needs to be associated with corresponding geo-graphical areas. Textual descriptions, he felt, wouldcertainly help visually impaired users to quicklygain an overview of the underlying data providedthey present the same information a sighted personextracts from a visual representation of the data.3.1 Parallel CorpusIn order to model the process of mapping geo-referenced data to its textual description we need3See www.csd.abdn.ac.uk/research/roadsafe.4See www.grampianblind.co.ukto understand how humans perform this activity.There is a huge amount of geo-referenced data on-line, and we start by considering a few such ex-pert (i.e., statistician) written texts and their corre-sponding data, which can be found online via organ-isations like National Statistics. Using such paral-lel data and text corpora for knowledge acquisition(KA) is a fairly common approach (Sripada et al,2003). An example from Lambeth Council’s web-site of the sort of texts we are looking at follows be-low, and the corresponding data appears in Table 1.Such pairings of sentences and tables containing thedata communicated in the sentences form our grow-ing corpus. The corpus collection is in an initialstage, and we currently have about 300 such pairsof sentences and data tables gleaned from a handfulof online documents.(1) Example 1: People from Non-white ethnicgroups constituted 30.3 % of Lambeth’spopulation in 1991, compared with 6.1% of thecountry as a whole.(2) Example 2: Black African residents haveincreased in Lambeth by 5.1%. This is alsoreflected London wide and nationally.We distinguish content depending on whether itclassifies strictly spatial data (e.g., the first sentencein Ex. 2), compares areas (e.g., the second sentencein Ex. 2 and the last phrase in Ex. 1), or com-pares spatial data across different times. Anotherway we can distinguish message content has to dowith whether messages describe factual informationor communicate inferences. Following (Law et al,2005), we distinguish between descriptive messageswhich are based on data analysis and factual fea-tures of the data, and interpretative messages whichinvolve expert knowledge about the domain, whichcan include everything from inferences on the datato the communication of specific domain knowl-edge. The interpretative features which arise gener-ally involve inferences drawn by the experts aboutthe data, e.g., explanations, cause-effect, etc. Al-though performing these sorts of inferences is cur-rently beyond the project’s scope, our initial studiesshow a predominance of descriptive messages likethose in Ex. 1 and 2, which seems to indicate thataddressing these sorts of messages will enable us toaddress a decent proportion of the sorts of messageswe want to communicate. Another point to bear inmind is that the documents we’re webscraping aredesigned for sighted users, which means that onecannot assume that the modalities of text and dataWhite Black CaribbeanBlack AfricanBlack OtherIndian Pakistani BangladeshiChinese1991200119912001 19912001 19912001 199120011991200119912001 19912001Lambeth 69.7 62.4 12.6 12.1 6.5 11.6 2.7 4.9 2.1 2 0.8 1 0.7 0.8 1.3 1.3Inner London 74.4 65.7 7.1 6.9 4.4 8.3 2 3.3 3 3.1 1.2 1.6 2.8 4.6 1.1 1.4Greater London79.8 71.2 4.4 4.8 2.4 5.3 1.2 2.3 5.2 6.1 1.3 2 1.3 2.1 0.8 1.1Table 1: The Corresponding Data for Examples 1 and 2contain the same information. However, althoughthe two modes often contain complementary infor-mation, informal analysis indicates that official re-porting of geo-referenced data often contains bothmaps and text to communicate important informa-tion, making webscraping fairly viable for corpuscollection.3.2 Corpus AnalysisWe index data tables like the one shown in Table 1to the corresponding texts which describe them ina database, thereby linking information in differentmodalities and forming a parallel corpus. Essen-tially the core of KA from corpus analysis lies inthe mapping from a (manually interpreted) abstractrepresentation (AR) of an expert textual descriptionor message (Reiter and Dale, 2000) to an AR ofthe data, as is shown in Figure 3. Note that thetextual analysis takes place on the sentential levelcurrently; we leave higher-level discourse structurefor future work. Abstract representations of data(ADR) contain the results of applying data analysismethods (which can range from simple descriptivestatistics to spatial data mining methods) on the rawdata found in tables in the documents.AR of textual descriptions proceeds in severalsteps. We started building up an ontology ofspatial relations by labelling texts at the senten-tial level according to the messages communicated;these message labels abstract over texts and in-dicate the primary spatial relation communicated,e.g., category : location − density, which for ex-ample labels the text: “The Black Caribbean com-munity is concentrated in the wards around Brix-ton”. Here category is a higher level class inthe ontology corresponding to messages communi-cating information to do with a category of data,in this case the Black Caribbean population, andlocation − density indicates the particular feature(location) and property of this feature (density)under discussion. Message labels indicate infor-mation content. We indicate sentential rhetori-cal/discourse content via a set of around ten mes-sage predicates adopted from McKeown’s messagepredicates (McKeown, 1985) and RST (Mann andThompson, 1988). These message predicates com-municate contrast, causality, etc. Message predi-cates and message labels constitute the highest levelof abstraction at the sentential level in our ATR.At a lower level we create ARs for the mes-sages which can be seen as an intermediary levelof abstraction between message labels and predi-cates and the texts themselves and correlate roughlyto cue-phrases. We find all cue-phrases which in-dicate spatial relations in the corpora, e.g., “con-stituted”, “concentrated”, to get a range of thesorts of contexts these cues can appear in andthen abstract over the cues, forming mostly syn-onymous classes of cues which can be associatedwith the same message labels and predicates. Thetext above has as its message content, the frameconcentrated[Group,Location].3.2.1 Initial Methodology and FindingsWe map from ATRs to ADRs by (manually) asso-ciating simple sequences of data analysis methods(which we term macros) with ATRs, so the previ-ous text’s ATR is associated with the method spatialsegmentation. The text “A higher number of BlackAfrican people live in the north of the borough thanin the south” would be associated with first spatialsegmentation, and then, given that the distributionis not uniform, summation of the population valuesfor the category in geographic regions with differentfrequencies.The macros serve as an initial indication of whichdata analysis methods we should apply to the dataand in which order so that we can generate the kindsof texts produced by the experts. We expect thatthe findings from data analysis will mostly lie at thesentential level, e.g., descriptive statistical informa-tion, or information from spatial segmentation. Theidea is that these mappings between data and textcan be used as a backbone for describing new datasets which share similar findings from spatial dataanalysis.From an initial analysis of 70 texts, we found thatspatial segmentation was the most common methodinvoked, occurring 17% of the time, followed bycomparisons of values, which occurred 14% of thetime. We found that 13% of the texts involved asequence of data analysis methods, e.g., segmenta-Figure 2: The Text to Data Mapping Processtion followed by breakdown analysis, and another13% invoked multiple (non-sequential) data analy-sis methods like reporting the minimum and maxi-mum values. This leaves the majority (74%) as sim-ply invoking a single data analysis method. Ignor-ing whether methods are invoked sequentially or inmultiples, we found that the largest number of meth-ods invoked involve simply giving significant val-ues, ranking values, summing them or comparingthem. However the extent to which these findings(as opposed to others arising from the same data)are significant needs to be resolved so that given adata set, we can determine which findings are “in-teresting”.Initial Implementation We implement our ATRin the SimpleNLG framework and toolkit5, enablingus to generate text from ATRs. While much workstill needs to be done toward implementing the datato ADR and ADR to ATR stages, this work imple-ments one third of the Atlas system.4 Future WorkThis paper describes work in progress on the At-las.txt project, which just started in January 2007.As such, our goal here is to introduce the projectand initial KA work in the hopes that we will re-ceive useful feedback about our initial methodologywhich will then guide future work. In this paperwe have presented initial ideas about mapping geo-spatial data to text via the data analysis macros nec-essary for communicating the corresponding textswith the goal of driving generation of similar sen-tences for unseen data. We still need to account fordiscourse level structuring of these messages.An immediate area of future work involvesanalysing more corpora in order to expand our set ofdata analysis macros. We also need to specificallyinvestigate the extent to which the data analysismacros we associate with ATRs actually do producethe information communicated in the texts. Addi-tionally we need a better understanding of the in-formational requirements of visually-impaired end-users, and the extent to which results from a surveywe are currently running including “think aloud”5See www.csd.abdn.ac.uk/e˜reiter/simplenlg/.descriptions of census data from sighted users needsto be adapted for the visually-impaired.ReferencesS. Boyd. 1997. 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