Automated scoring by means of Latent Semantic Analysis (LSA) has been introduced lately to improve the traditional human scoring system. The purposes of the present study were to develop a LSA-based assessment system to evaluate children’s Chinese sentence construction skills and to examine the effectiveness of LSA-based automated scoring function by comparing it with traditional human scoring....

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Traditional semantic theories assume that meaning arises from the syntactic combination of amodal symbols processed by a modular subsystem. This idea has two striking implications: first, sensory-motor experience has no relevance in language processing; secondly, since the domain of syntactic rules is the sentence, linguistic interpretation takes place in a two-step fashion such that discourse-...

Making speech recognition output readable is an important task. The first step here is automatic sentence end detection (SED). We introduce novel F0 derivative-based features and sentence end distance features for SED that yield significant improvements in slot error rate (SER) in a multi-pass framework. Three different SED approaches are compared on a spoken lecture task: hidden event language...

We propose a novel approach to context sensitive semantic smoothing by making use of an intermediate, ”semantically light” representation for sentences, called Semantically Relatable Sequences (SRS). SRSs of a sentence are tuples of words appearing in the semantic graph of the sentence as linked nodes depicting dependency relations. In contrast to patterns based on consecutive words, SRSs make ...

The effect of semantic priming upon lexical decisions made for words in isolation (Experiment 1) and during sentence comprehension (Experiment 2) was investigated using a crossmodal lexical decision task. In Experiment 1, subjects made lexical decisions to both auditory and visual stimuli. Processing auditorily presented words facilitated subsequent lexical decisions on semantically related vis...

Existing machine translation decoding algorithms generate translations in a strictly monotonic fashion and never revisit previous decisions. As a result, earlier mistakes cannot be corrected at a later stage. In this paper, we present a translation scheme that starts from an initial guess and then makes iterative improvements that may revisit previous decisions. We parameterize our model as a c...

Structural ambiguity in a sentence cannot be resolved without semantic help. We present a process for structural disambiguation that uses verb expectations, presupposition satisfaction, and plausibility, and an algorithm for making the final choice when these cues give conflicting information. The process, called the Semantic Enquiry Desk, is part of a semantic interpreter that makes sure all i...