Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from

neurons The human brain consists of about 10 11 neurons of various types; each neuron typically connects, via an axon that eventually branches out into strands and substrands, to many thousand neurons. The firing of a neuron is mostly an all-or-nothing business; this discrete character is retained as the pulse travels down an axon. However, upon arrival to a destination neuron the pulse is handled by a synaptic interface characterized by an analog parameter (typically, an excitation or inhibition weight) whose value may be to some extent history-dependent. The complete physiological picture is rather complex. A drastically simplified model of a neuron, proposed by McCulloch and Pitts[38], is shown in Fig. 5. The neuron can be in one of two states, +1 and \Gamma1, which may be thought of as `on' and `off', or `true' and `false'; this state appears at the neuron's output. The inputs may come from other neurons or from external stimuli. State updating may be synchronous (all neurons a...

high gray-level resolution monochrome displays with

this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash- coding methods are examined and compared with a particular conventional

Considerable evidence indicates that domain specific knowledge in the form of schemes is the primary factor distinguishing experts from novices in problem-solving skill. Evidence that conventional problem-solving activity is not effective in schema acquisition is also accumulating. It is suggested

characteristics of large scale computational uid dynamics applications. The principal distinguishing feature of these benchmarks is their \pencil and paper " speci cation | all details of these benchmarks are speci ed only algorithmically. In this way many of the di culties associated with conventional

We present a broad extension of the conventional formalism of state machines and state diagrams, that is relevant to the specification and design of complex discrete-event systems, such as multi-computer real-time systems, communication protocols and digital control units. Our diagrams, which we

. Slots unite variables and procedures into a single construct. This permits the inheritance hierarchy to take over the function of lexical scoping in conventional languages. Finally, because SELF does not distinguish state from behavior, it narrows the gaps between ordinary objects, procedures

Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense. In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming. We give a precise methodology for studying this phenomenon from

We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling