1. A computer-implemented hypotheses generating device for generating causal hypotheses from a prescribed corpus of text located on the Internet and outputting a causality hypothesis associated with an inputted query in order to improve understanding of natural language in artificial intelligence by recognizing a relation between sentences or phrases of natural language, wherein: each predicate template is coupled to a noun to form a phrase; an excitation value indicative of orientation and magnitude of excitation is assigned to each predicate template in accordance with a classification of excitatory, inhibitory and neutral; excitatory indicates a description of an event in an orientation to stimulate a function or effect of an object indicated by a noun coupled to an associated predicate template, inhibitory indicates a description of an event in an orientation not to stimulate a function or effect of an object indicated by a noun coupled to the associated predicate template; neutral indicates that the associated predicate template is neither an excitatory nor inhibitory predicate template; excitatory and inhibitory nature of the associated predicate template is referred to as a polarity; and said hypotheses generating device includes: a network interface connected to the Internet; a non-transitory computer readable medium configured to store the generated hypotheses, the non-transitory computer readable medium comprising: a conjunction storage configured to store conjunctions classified to and/thus type or but type conjunctions; and a processor configured to: perform expression matching to collect, from the prescribed corpus of natural language accessed via the Internet, noun pairs satisfying a certain relation between a first noun and a second noun forming the noun pair, wherein the certain relation comprises at least one of: a causality relation where the first noun causes the second noun; a material relation noun pair where the first noun is a material of the second noun; or a preventive relation where the first noun prevents the second noun; classify a polarity of relation between nouns forming each noun pair to positive or negative, such that the polarity of relation between nouns forming each noun pair is: defined as positive if an object represented by one of the pair of nouns promotes occurrence of an object represented by the other, and defined as negative if the object represented by the one of the pair of nouns prevents the occurrence of the object represented by the other; for each of the collected noun pairs: collect first predicate templates that form a dependency with the first noun of the noun pair in the prescribed corpus of natural language; collect second predicate templates that form a dependency with the second noun of the noun pair in the prescribed corpus of natural language; combining the first predicate templates and the second predicate templates to form predicate template pairs; determine, for each of the predicate template pairs, whether both templates of a given predicate template pair belong to the same excitatory/inhibitory class or to the opposite excitatory/inhibitory class, based on the polarity of relation of the noun pair co-occurring with the predicate template pair and the conjunction coupling the predicate template pair; when the relation between the noun pair is classified as positive, selecting from the predicate template pairs at least one predicate template pair, wherein: each of the selected at least one predicate template pair frequently co-occur in the prescribed corpus of natural language with conjunctions that are classified in the conjunction storage as and/thus type conjunctions, wherein frequently co-occurring refers to the frequency of occurrence in the prescribed corpus of natural language being greater than a prescribed value; and the first predicate template and the second predicate template of each of the selected at least one predicate template pair belong to the same excitatory/inhibitory class; when the relation between the noun pair is classified as negative, selecting from the predicate template pairs at least one predicate template pair, wherein: each of the selected at least one predicate template pair frequently co-occur in the prescribed corpus of natural language with conjunctions that are classified in the conjunction storage as and/thus type conjunctions; and the first predicate template and the second predicate template of each of the selected at least one predicate template pair belong to the opposite excitatory/inhibitory class; for each of the selected at least one predicate template pair: generating a causality hypothesis by attaching the template pair to the noun pair; storing the causality hypothesis in the non-transitory computer readable medium; upon receiving an input query, retrieve stored causality hypotheses including a noun of said input query; determine, for each of said retrieved causality hypotheses, the polarities of the first predicate template, the second predicate template, the first noun, and the second noun forming the causality hypothesis; determine whether each of said causality hypotheses is relevant to the input query based on the polarities of said first and second predicate templates and of said first and the second nouns; and output the causality hypothesis determined to be relevant in order to improve machine based estimate of consequences of the inputted query by the computer-implemented hypotheses generating device.