for a theory of language compre- third section we consider processing issues: how information is hension is to explain how the reader or listener copes with a processed within the mental lexicon and how contextual inforpervasive ambiguity problem. Languages are structured at mation can influence

on the species in question. In this paper, we introduce a method to exploit contextual information in an abstract, like tissue type, chromosome location, etc., to tackle this problem. Using this technique, we have been able to improve system performance (Fscore) by 14.3 % on the BioCreAtIvE-II GN task test set

of TFIDF weighting is proposed and a Chinese thesaurus is additionally utilized to cope with data sparseness problem. We select 90 frequent cases of covering ambiguities as the target. The training set includes 77654 sentences, and the test set includes 19242 sentences. The experimental results showed

. Ambiguity is one of the most difficult problems in natural language processing. We present a dynamic semantics which gives a formal account of the behavior of ambiguous expressions occurring in a sequence of sentences. Ambiguous sentences are represented in an underspecified way and the update

. This paper addresses this problem within the dynamical systems framework. The idea is to treat the agent in its environment as a dynamical system, and augment the original state space using contextual cues extracted empirically as the agent exercises existing control policies. Contextual cues

, exiting and intersec-tion make the prediction process degrade in precision; 3) Visual ambiguities among nearby targets make the trackers error prone. In this paper, we address the MOT problem by embedding con-textual proposal distributions and contextual observation models into a mixture tracker which

to infer plausible conclusions. We show that this local way to dealing with default information allows us to correctly handle the well-known problems of specificity, irrelevance, blocking of inheritance, ambiguity, and redundancy. 1. INTRODUCTION One of the most important problem encountered in knowledge

. In order to quantify context, we formulate a new context risk function and a maximum margin context (MMC) model to solve the minimization problem of the risk function. Crucially, the usefulness and goodness of contextual information is evaluated directly and explicitly through a discriminant context

provides an overview of Y!Q’s user interaction design, highlighting its novel aspects for capturing high quality search context. To address the second problem, Y!Q uses a semantic network for analyzing search context, possibly resolving ambiguous terms, and generating a contextual digest comprising its key

ambigu-ity problem arises. We study three approaches to solve this problem: using plain gettext (baseline condition), using get-text plus being able to operate the UI, and translating the UI in-place. We found that translators are substantially faster with plain gettext but commit a significantly higher