document: Automated construction of web accessibility models from transaction click-streams Screen readers , the dominant assistive technology used by visually impaired people to access the Web , function by speaking out the content of the screen serially . Using screen readers for conducting online transactions can cause considerable information overload , because transactions , such as shopping and paying bills , typically involve a number of steps spanning several web pages . One can combat this overload by using a transaction model for web accessibility that presents only fragments of web pages that are needed for doing transactions . We can realize such a model by coupling a process automaton , encoding states of a transaction , with concept classifiers that identify page fragments `` relevant '' to a particular state of the transaction . In this paper we present a fully automated process that synergistically combines several techniques for transforming unlabeled click-stream data generated by transactions into a transactionmodel . These techniques include web content analysis to partition a web page into segments consisting of semantically related content , contextual analysis of data surrounding clickable objects in a page , and machine learning methods , such as clustering of page segments based on contextual analysis , statistical classification , and automata learning . The use of unlabeled click streams in building transaction models has important benefits : ( i ) visually impaired users do not have to depend on sighted users for creating manually labeled training data to construct the models ; ( ii ) it is possible to mine personalized models from unlabeled transaction click-streams associated with sites that visually impaired users visit regularly ; ( iii ) since unlabeled data is relatively easy to obtain , it is feasible to scale up the construction of domain-specific transaction models ( e.g. , separate models for shopping , airline reservations , bill payments , etc. ) ; ( iv ) adjusting the performance of deployed models over timtime with new training data is also doable . We provide preliminary experimental evidence of the practical effectiveness of both domain-specific , as well as personalized accessibility transaction models built using our approach . Finally , this approach is applicable for building transaction models for mobile devices with limited-size displays , as well as for creating wrappers for information extraction from web sites .
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