CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage Entry of PCT/JP2017/040665 filed on Nov. 10, 2017, the contents of all of which are incorporated herein by reference, in their entirety.

TECHNICAL FIELD

The present invention relates to an information processing apparatus, an information processing method, and a computer-readable recording medium that includes a program recorded thereon for realizing the apparatus and method.

BACKGROUND ART

In recent years, development of systems that extract words relating to specific event information from a provided text has been ongoing.

For example, Non-Patent Document 1 discloses a method of extracting a word (hereinafter, referred to as a specific word) relating to specific event information from a provided text, with a system utilizing a convolutional neural network (hereinafter, also referred to as a CNN).

FIG. 15is a block diagram showing a configuration of an extraction apparatus that utilizes the method of Non-Patent Document 1. An extraction apparatus100shown inFIG. 15is provided with a lexical analysis unit101, an encoder102, a classification unit103, and an optimization processing unit104. In the extraction apparatus100, the classification unit103extracts a specific word from a provided text (hereinafter, referred to as an analysis text), with a determination model learned using training texts.

FIG. 16is a diagram showing part of a training text. When learning a determination model in the extraction apparatus100, first, a training text and position information indicating the position of the specific word in the training text are input to the lexical analysis unit101. Specifically, in the case where, for example, “transfer money” is provided as the specific event information and “paid” is provided as the specific word, the position of “paid” in the training text is input to the lexical analysis unit101together with the training text. The lexical analysis unit101generates a word string such as shown inFIG. 17, by performing lexical analysis of the input training text.

The encoder102extracts a plurality of words from the word string generated by the lexical analysis unit101, using a window W of a predetermined length L centering on a word105targeted for detection. More specifically, the lexical analysis unit101generates a plurality of word groups, by extracting a plurality of words in each position, while shifting the position of the window W.

Also, the encoder102generates a plurality of matrices corresponding to the plurality of word groups.FIG. 18is a diagram showing an example of a matrix that is generated by the encoder102. Note thatFIG. 18shows a matrix corresponding to a word group constituted by “was, booked, and, paid, for”. Referring toFIG. 18, the encoder102, by associating semantic vectors generated using a dictionary model with the plurality of words of each word group, generates a matrix in which the plurality of words and respective semantic vectors of the plurality of words are associated, for every word group. Note that, inFIG. 18, the portion representing the semantic vector of “was” is enclosed by a dashed-dotted line.

The classification unit103determines whether the word of each position of the word string (refer toFIG. 17) corresponds to the specific word, using the matrices generated by the encoder102and a determination model that uses a CNN. In other words, the classification unit103specifies the position of the specific word in the training text.

The optimization processing unit104updates parameters of the determination model, based on the determination result of the classification unit103and the actual position of the specific word in the training text. The determination model is thus learned in the extraction apparatus100. When an analysis text is provided to the lexical analysis unit101after the determination model is learned, matrices are generated in the above manner by the encoder102, and the specific word is extracted by the classification unit103, using the generated matrices and the learned determination model.

LIST OF RELATED ART DOCUMENTS

Non-Patent Document

Non-Patent Document 1: Y. Kim, 2014 “Convolutional Neural Networks for Sentence Classification” EMNLP 2014, Oct. 25, 2014, pp. 1746-1751

SUMMARY OF INVENTION

Problems to be Solved by the Invention

With the abovementioned extraction apparatus100that utilizes the method disclosed in Non-Patent Document 1, the surrounding context of the word targeted for detection can be taken into consideration, by increasing the length of the window W and extracting a plurality of words. The ambiguity of meaning inherent in the word targeted for detection is thereby resolved, and it can be determined whether the word targeted for detection corresponds to the specific word. However, as a result of investigations by the inventor of the present invention, it was revealed that the specific accuracy of the position of the specific word in the text may decrease, due to increasing the length of the window W.

An example object of the invention is to provide an information processing apparatus, an information processing method and a computer-readable recording medium that are able to accurately extract a word relating to specific event information from a provided text.

Means for Solving the Problems

An information processing apparatus according to an example aspect of the invention includes:

a lexical analysis circuit configured to acquire a training text and position information indicating a position, in the training text, of a word relating to specific event information, and generate a training word string given, for every word, position information indicating a position of the word in the training text and a label indicating whether the word corresponds to the word relating to the event information, by performing lexical analysis of the training text;

a pair generation circuit configured, while shifting, one word at a time from a head to an end of the training word string generated by the lexical analysis circuit, a position of a window of a predetermined length centering on a word targeted for detection, in the training word string, to extract a plurality of training word pairs obtained by respectively combining each word other than the word targeted for detection within the window in each position with the word targeted for detection;

a matrix generation circuit configured, by associating a semantic vector of each of the plurality of words of the training word string, generated using a dictionary model set in advance, with each word of the plurality of training word pairs, to generate, for each of the plurality of training word pairs, a training matrix in which each word and the semantic vector of the word are associated;

a classification circuit configured to calculate, for a word of each position of the training word string, a probability of the word corresponding to the word relating to the specific event information, using the plurality of training matrices generated by the matrix generation circuit and a determination model that uses a convolutional neural network; and

an optimization processing circuit configured, the probability being included in a plurality of probabilities, to update a parameter of the determination model, such that the probability of the word labeled as corresponding to the word relating to the event information is high, among the plurality of probabilities of words of the respective positions of the training word string calculated by the classification circuit.

Also, an information processing method according to an example aspect of the invention includes:

acquiring a training text and position information indicating a position, in the training text, of a word relating to specific event information, and generating a training word string given, for every word, position information indicating a position of the word in the training text and a label indicating whether the word corresponds to the word relating to the event information, by performing lexical analysis of the training text;

extracting a plurality of training word pairs obtained by respectively combining each word other than the word targeted for detection with the word targeted for detection within a window of a predetermined length centering on a word targeted for detection in each position, while shifting, one word at a time from a head to an end of the training word string, a position of the window, in the training word string;

generating, for each of the plurality of training word pairs, a plurality of training matrices in which each word and a semantic vector of the word are associated, by associating the semantic vector of each of a plurality of words of the training word string, generated using a dictionary model set in advance, with each word of the plurality of training word pairs;

calculating, for a word of each position of the training word string, a probability of the word corresponding to the word relating to the specific event information, using the plurality of training matrices and a determination model that uses a convolutional neural network; and

updating, the probability being included in a plurality of probabilities, a parameter of the determination model, such that the probability of the word labeled as corresponding to the word relating to the event information is high, among the plurality of probabilities of words of the respective positions of the training word string.

Furthermore, a non-transitory computer-readable recording medium according to an example aspect of the invention includes a program recorded thereon, the program including instructions that cause the computer to carry out:

acquiring a training text and position information indicating a position, in the training text, of a word relating to specific event information, and generating a training word string given, for every word, position information indicating a position of the word in the training text and a label indicating whether the word corresponds to the word relating to the event information, by performing lexical analysis of the training text;

extracting a plurality of training word pairs obtained by respectively combining each word other than the word targeted for detection with the word targeted for detection within a window of a predetermined length centering on a word targeted for detection in each position, while shifting, one word at a time from a head to an end of the training word string, a position of the window, in the training word string;

generating, for each of the plurality of training word pairs, a plurality of training matrices in which each word and a semantic vector of the word are associated, by associating the semantic vector of each of a plurality of words of the training word string, generated using a dictionary model set in advance, with each word of the plurality of training word pairs;

calculating, for a word of each position of the training word string, a probability of the word corresponding to the word relating to the specific event information, using the plurality of training matrices and a determination model that uses a convolutional neural network; and

updating, the probability being included in a plurality of probabilities, a parameter of the determination model, such that the probability of the word labeled as corresponding to the word relating to the event information is high, among the plurality of probabilities of words of the respective positions of the training word string.

Advantageous Effects of the Invention

As described above, according to the invention, a word relating to specific event information can be accurately extracted from a provided text.

