Latent semantic analysis (LSA) has been used in several intelligent tutoring systems(ITS's) for assessing students' learning by evaluating their answers to questions in the tutoring domain. It is based on word-document cooccurrence statistics in the training corpus and a dimensionality reduction technique. However, it doesn't consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance of an ITS. We present here an approach called Syntactically Enhanced LSA (SELSA) which generalizes LSA by considering a word along with its syntactic neighborhood given by the part-of-speech tag of its preceding word, as a unit of knowledge representation. The experimental results on Auto-Tutor task to evaluate students' answers to basic computer science questions by SELSA and its comparison with LSA are presented in terms of several cognitive measures. SELSA is able to correctly evaluate a few more answers than LSA but is having less correlation with human evaluators than LSA has. It also provides better discrimination of syntactic-semantic knowledge representation than LSA.

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Statistical language models using n-gram approach have been under the criticism of neglecting large-span syntactic-semantic information that influences the choice of the next word in a language. One of the approaches that helped recently is the use of latent semantic analysis to capture the semantic fabric of the document and enhance the n-gram model. Similarly there have been some approaches that used syntactic analysis to enhance the n-gram models. In this paper, we explain a framework called syntactically enhanced latent semantic analysis and its application in statistical language modeling. This approach augments each word with its syntactic descriptor in terms of the part-of-speech tag, phrase type or the supertag. We observe that given this syntactic knowledge, the model outperforms LSA based models significantly in terms of perplexity measure. We also present some observations on the effect of the knowledge of content or function word type in language modeling. This paper also poses the problem of better syntax prediction to achieve the benchmarks.

Several applications of practical interest stem from the capability to monitor and store packet-level traces in a 3G network. Among them, the possibility to infer and locatenetwork problems (e.g. persistent shortage of capacity, or equipment misfunctioning), in the core and radio sections, without direct access to the equipments. This approach yields strong practical benefits, given the costs and complexity of accessing network equipments, especially in the Radio Access Network. At the same time, it exposes practical issues -e.g. the need to dynamically locate the traffic sources (Mobile Stations) -and theoretical problems -e.g. inferring congested cells from Routing-Area level TCP measurements. We report on our work-in-progress aimed at implementing such mechanisms on top of an advanced monitoring system now deployed in an operational network.