Learning Analogies and Semantic Relations

We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the Scholastic Aptitude Test (SAT). A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct). We motivate this research by relating it to work in cognitive science and linguistics, and by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as "laser printer", according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for these challenging problems

Measuring praise and criticism: Inference of semantic orientation from association

The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words

Do all inhibitions act alike? A study of go/no-go and stop-signal paradigms

Response inhibition is frequently measured by the Go/no-go and Stop-signal tasks. These two are often used indiscriminately under the assumption that both measure similar inhibitory control abilities. However, accumulating evidence show differences in both tasks' modulations, raising the question of whether they tap into equivalent cognitive mechanisms. In the current study, a comparison of the performance in both tasks took place under the influence of negative stimuli, following the assumption that ''controlled inhibition'', as measured by Stop-signal, but not ''automatic inhibition'', as measured by Go/no-go, will be affected. 54 young adults performed a task in which negative pictures, neutral pictures or no-pictures preceded go trials, no-go trials, and stop-trials. While the exposure to negative pictures impaired performance on go trials and improved the inhibitory capacity in Stop-signal task, the inhibitory performance in Go/no-go task was generally unaffected. The results support the conceptualization of different mechanisms operated by both tasks, thus emphasizing the necessity to thoroughly fathom both inhibitory processes and identify their corresponding cognitive measures. Implications regarding the usage of cognitive tasks for strengthening inhibitory capacity among individuals struggling with inhibitory impairments are discussed

The influence of radiation shielding on reusable nuclear shuttle design

Alternate reusable nuclear shuttle configurations were synthesized and evaluated. Particular attention was given to design factors which reduced tank exposure to direct and scattered radiation, increased payload-engine separation, and improved self-shielding by the LH2 propellant. The most attractive RNS concept in terms of cost effectiveness consists of a single conical aft bulkhead tank with a high fineness ratio. Launch is accomplished by the INT-21 with the tank positioned in the inverted attitude. The NERVA engine is delivered to orbit separately where final stage assembly and checkout are accomplished. This approach is consistent with NERVA definition criteria and required operating procedures to support an economically viable nuclear shuttle transportation program in the post-1980 period

Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., "honest", "intrepid") and a negative semantic orientation implies undesirability (e.g., "disturbing", "superfluous"). This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the results. The algorithm is empirically evaluated using a training corpus of approximately one hundred billion words — the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou and McKeown (1997), using a complex four-stage supervised learning algorithm that is restricted to determining the semantic orientation of adjectives