TECHNICAL FIELD

The present invention relates to an apparatus for extracting causality from expressions existing in natural language text and, more specifically, to a technique for recognizing and extracting causality expressed without explicit clue terms in text described in a natural language.

BACKGROUND ART

Automatic recognition of event causalities written in Web texts and the like is an important technique, for example, to automatically generate future scenarios to enable right decision making. Highly precise recognition, however, has been difficult because event causality is expressed in a wide range of forms. For example, the sentence “an earthquake occurred, and tsunami came” is considered to clearly express a causality including a cause “an earthquake occurred” and an effect “tsunami came.” This sentence, however, does not include any explicit clue term such as a conjunction “because” or “since.” Conventional art recognizes causalities based on clue term related to causality and, therefore, it is difficult to recognize causality without such clue terms. It is important to extract causalities with a high precision also from such texts, in order to accumulate pieces of knowledge related to wider range of causalities, to comprehend documents, to develop why-type question answering and to generate future scenarios and so on.

Prior art in this field includes a technique of recognizing phrase pairs representing a cause and an effect of causalities using not only such clue terms but also some language expression patterns, as well as automatic recognition technique of cause and effect phrase pairs using machine learning based on language expressions rather than patterns.

CITATION LIST

Non Patent Literature

NPL 1: Chikara Hashimoto, Kentaro Torisawa, Julien Kloetzer, Motoki Sano, Istvan Varga, Jong-Hoon Oh, and Yutaka Kidawara. 2014. Toward future scenario generation: Extracting event causality exploiting semantic relation, context, and association features. In Proceedings of ACL, pages 987-997.NPL 2: Jong-Hoon Oh, Kentaro Torisawa, Chikara Hashimoto, Motoki Sano, Stijn De Saeger, and Kiyonori Ohtake. 2013. Why-question answering using intra- and inter-sentential causal relations. In Proceedings of ACL, pages 1733-1743.

SUMMARY OF INVENTION

Technical Problem

The conventional methods for causality recognition, however, still suffer from low precision and limited coverage. The reason for this may be that causality is expressed in a wide range of forms that often lack explicit clue terms indicating causality, and that a large number of patterns may indicate causalities.

Consider, for example, the following two sentences.

1. Typhoons have strengthened because global warming has progressed.

2. Global warming progressed, and typhoons strengthened.

The first sentence includes the word “because,” which explicitly indicates the causality between the effect “typhoons have strengthened” and the cause “global warming has progressed.” By contrast, the second sentence has no such clue terms. Nonetheless, many people would apprehend that the sentence expresses the same causality as the first sentence. This is possible because people have background knowledge about the “typhoons” and the “global warming.” Since causalities are often expressed without explicit clue terms, it is necessary to recognize the causalities in the second type of sentences with a high precision.

Therefore, an object of the present invention is to provide a causality recognizing apparatus capable of recognizing causality expressions with a high precision from natural language texts regardless of whether or not there is any clue term indicating a causality.

Solution to Problem

According to a first aspect, the present invention provides a causality recognizing apparatus automatically recognizing a causality represented by first and second phrases found in text. The first phrase is a candidate of a cause part of the causality, and the second phrase is a candidate of an effect part of the causality. The first and second phrases both include a combination of a noun-phrase and a predicate. The causality recognizing apparatus includes: a first vector generating means configured to receive a causality candidate including the first and second phrases, for generating a set of first word vectors each representing a word sequence forming the causality candidate; a second vector generating means for generating second word vectors respectively representing word sequences forming contexts in which the first and second phrases appear in the text; a related passage vector generating means for generating a word vector representing background knowledge for determining presence/absence of causality between the noun-phrase included in the first phrase and the noun-phrase included in the second phrase; and a classifying means pre-trained to output, by receiving the first and second word vectors and the word vector generated by the related passage vector generating means, an index indicating that the causality candidate from which the word vectors are derived represents causality. The background knowledge vector generating means includes an arbitrary combination including at least one of: a third word vector generating means for collecting a pattern including the noun-phrase included in the first phrase, the noun-phrase included in the second phrase and a predicate connecting these noun-phrases from a large number of documents, and for generating a third word vector representing a word sequence of the pattern; a fourth vector generating means generating a why-type question from the causality candidate, receiving a set of answers to the why-type question from a why-type question answering apparatus, and for generating a fourth word vector representing a word sequence including at least the noun-phrase included in the first phrase and the noun-phrase included in the second phrase, from an answer including both the noun-phrase included in the first phrase and the noun-phrase included in the second phrase, among the answers in the set of answers; and a fifth vector generating means for generating, from a passage as a set of prescribed number of consecutive sentences existing in a large number of documents, in which the noun-phrase included in the first phrase, the noun-phrase included in the second phrase and a clue term related to causality co-occur, a fifth word vector representing a word sequence concatenating the noun-phrases included in the first and second phrases, a word representing dependency between these in the set of sentences and the clue term included in the set of sentences.

Preferably, the classifying means includes a multicolumn neural network having a plurality of columns and learned in advance to receive the first and second word vectors and the word vector output from the background knowledge vector generating means at sub-networks of different columns respectively and to output an index indicating that the causality candidate from which the word vectors are derived represents causality.

More preferably, the third vector generating means includes: a binary pattern collecting means for collecting, from the large number of documents, a binary pattern formed of a pattern including the noun-phrase included in the first phrase, the noun-phrase included in the second phrase, and the predicate connecting the noun-phrases; a frequency counting means for counting frequency of appearance of the binary pattern collected by the binary pattern collecting means; and means for generating a word vector representing a word sequence forming a prescribed number of binary patterns having top frequency counts counted by the frequency counting means from the binary patterns collected by the collecting means and for outputting the word vector as the third word vector.

More preferably, the fourth vector generating means includes: an answer obtaining means for generating a why-type question by adding an interrogative representing a why-type question to the second phrase and applying it to the why-type question answering apparatus, thereby obtaining a set of answers from the why-type question-answering apparatus; an extracting means for extracting a prescribed number of answers including both the noun-phrase included in the first phrase and the noun-phrase included in the second phrase, from the set of answers obtained by the answer obtaining means; a dependency analysis means for analyzing dependency of each of the answers extracted by the extracting means and for generating a dependency structure; and a means for generating, for each of the answers extracted by the extracting means, a word vector representing a word sequence including the noun-phrases included in the first and second phrases and a word at a position of a prescribed relation on the dependency structure generated by the dependency analysis means, and for outputting it as the fourth vector.

The fifth word vector generating means includes: a retrieving means for retrieving, from a large number of documents, a passage as a set of prescribed number of consecutive sentences, in which the noun-phrase included in the first phrase, the noun-phrase included in the second phrase and a clue term related to causality co-occur; a dependency analysis means for analyzing dependency of the passage retrieved by the retrieving means and generating a dependency structure; and a means for generating a fifth word vector from a word sequence by concatenating a word existing on a path between the noun-phrases included in the first and second phrases in the dependency structure generated by the dependency analysis means and the clue term included in the passage.

According to a second aspect, the present invention provides a computer program causing a computer to function as any of the above-described causality recognizing apparatuses.

