We describe an approach to retrieval of documents that contain of both free text and semantically enriched markup. In particular, we present the design and implementation prototype of a framework in which both documents and queries can be marked up with statements in the DAML+OIL semantic web language. These statements provide both structured and semi-structured information about the documents and their content. We claim that indexing text and semantic markup together will significantly improve retrieval performance. Our approach allows inferencing to be done over this information at several points: when a document is indexed, when a query is processed and when query results are evaluated.
In the past few years, the explosive growth of the Internet has allowed the construction of virtual systems containing hundreds or thousands of individual, relatively inexpensive computers. The agent paradigm is well-suited for this environment because it is based on distributed autonomous computation. Although the definition of a software agent varies widely, some common features are present in most definitions of agents. Agents should be autonomous, operating independently of their creator(s). Agents should have the ability to move freely about the Internet. Agents should be able to adapt readily to new information and changes in their environment. Finally, agents should be able to communicate at a high level, in order to facilitate coordination and co-operation among groups of agents. These aspects of agency provide a dynamic framework for the design of distributed systems.
After several decades of effort, significant progress has been made in the area of speech recognition technologies, and various speech-based applications have been developed. However, current speech recognition systems still generate erroneous output, which hinders the wide adoption of speech applications. Given that the goal of error-free output can not be realized in near future, mechanisms for automatically detecting and even correcting speech recognition errors may prove useful for amending imperfect speech recognition systems. This dissertation research focuses on the automatic detection of speech recognition errors for monologue applications, and in particular, dictation applications.
Due to computational complexity and efficiency concerns, limited linguistic information is embedded in speech recognition systems. Furthermore, when identifying speech recognition errors, humans always apply linguistic knowledge to complete the task. This dissertation therefore investigates the effect of linguistic information on automatic error detection by applying two levels of linguistic analysis, specifically syntactic analysis and semantic analysis, to the post processing of speech recognition output. Experiments are conducted on two dictation corpora which differ in both topic and style (daily office communication by students and Wall Street Journal news by journalists).
To catch grammatical abnormalities possibly caused by speech recognition errors, two sets of syntactic features, linkage information and word associations based on syntactic dependency, are extracted for each word from the output of two lexicalized robust syntactic parsers respectively. Confidence measures, which combine features using Support Vector Machines, are used to detect speech recognition errors. A confidence measure that combines syntactic features with non-linguistic features yields consistent performance improvement in one or more aspects over those obtained by using non-linguistic features alone.
Semantic abnormalities possibly caused by speech recognition errors are caught by the analysis of semantic relatedness of a word to its context. Two different methods are used to integrate semantic analysis with syntactic analysis. One approach addresses the problem by extracting features for each word from its relations to other words. To this end, various WordNet-based measures and different context lengths are examined. The addition of semantic features in confidence measures can further yield small but consistent improvement in error detection performance. The other approach applies lexical cohesion analysis by taking both reiteration and collocation relationships into consideration and by augmenting words with probability predicted from syntactic analysis. Two WordNet-based measures and one measure based on Latent Semantic Analysis are used to instantiate lexical cohesion relationships. Additionally, various word probability thresholds and cosine similarity thresholds are examined. The incorporation of lexical cohesion analysis is superior to the use of syntactic analysis alone.
In summary, the use of linguistic information as described, including syntactic and semantic information, can provide positive impact on automatic detection of speech recognition errors.
Semantic Web markup languages will improve the automated gathering and processing of information and help integrate multiagent systems with the existing information infrastructure. The authors, describe their ITtalks system and discuss how Semantic Web concepts and DAML+OIL extend its ability to provide an intelligent online service.
Jackal is a Java-based tool for communicating with the KQML agent communication language. Some features which make it extremely valuable to agent development are its conversation management facilities, flexible, blackboard style interface and ease of integration. Jackal has been developed in support of an investigation of the use of agents in shop oor information ow. This paper describes Jackal at a surface and design level, and presents an example of its use in agent construction.
For initial ranked retrieval, we continue to use a statistical language model to compute query/document similarity values. Hiemstra and de Vries [3] describe such a linguistically motivated probabilistic model and explain how it relates to both the Boolean and vector space models. The model has also been cast as a rudimentary Hidden Markov Model [4]. Although the model does not explicitly incorporate inverse document frequency, it does favor documents that contain more of the rare query terms. The similarity measure can be computed as
Information retrieval technology has been central to the success of the Web. For semantic web documents or annotations to have an impact, they will have to be compatible with Web based indexing and retrieval technology. We discuss some of the underlying problems and issues central to extending information retrieval systems to handle annotations in semantic web languages. We also describe three prototype systems that we have implemented to explore these ideas.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)