SEMANTIC REPRESENTATION GENERATION METHOD, SEMANTIC REPRESENTATION GENERATION DEVICE, AND RECORDING MEDIUM

In a semantic representation generation method, syntax data is generated by a superficial analysis on text data described in a natural language. A concept tag is provided to each morpheme based on the syntax data with reference to a CT system table in which concept information hierarchically and ambiguously representing a meaning of the morpheme for a part of speech. Provided is a first semantic tag indicating semantic information representing a semantic relation between a first phrase/sequence of phrases and a second phrase/sequence of phrases to a first pair made up of the first phrase/sequence of phrases corresponding to a predicate and the second phrase/sequence of phrases having a modification relation with the predicate based on the syntax data. Semantic representation data is generated based on the concept tag provided to each morpheme and the first semantic tag provided to the first pair.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to a method and a device for generating semantic representation data necessary for obtaining knowledge from text data such as a document described in a natural language.

Description of the Background Art

Recently studied and developed is a method for obtaining knowledge from various types of text data (referred to as “natural language data” hereinafter) such as a document described in a natural language by a computer. Also studied and developed is a method for structuring and storing the knowledge obtained in such a manner to generate a knowledge base, and answering a question in the natural language based on the knowledge base upon receiving the question in a computer.

A meaning of a word included in a sentence needs to be hierarchically and ambiguously captured in a semantic analysis of natural language data by a computer to appropriately obtain the knowledge and answer the question using the natural language as with the case described above. In response to this, a concept of a specific representation included in natural language data has been conventionally defined hierarchically (for example, refer to Koichi Takeuchi, Alastair Butler, Iku Nagasaki, Takuya Okamura, Prashant Pardeshi, “Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus”, Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pp. 3153-3161, Marseille, 11-16 May 2020).

Performed is an analysis of a structure of a sentence from a viewpoint of a relationship between an argument as a complement necessary for a predicative to make sense and a predicative (referred to as “predicate-argument structure”) while treating the predicative such as a verb or an adjective as a central meaning to capture the meaning of the sentence in a semantic interpretation on natural language data (for example, refer to Koichi Takeuchi, Masayuki Ueno, and Nao Takeuchi, “Annotating Semantic Role Information to Japanese Balanced Corpus”, Proceedings of MAPLEX 2015, 2015).

In addition, Japanese Patent Application Laid-Open No. 2021-111303 and Japanese Patent Application Laid-Open No. 6-195383 relate to the present invention.

SUMMARY

A meaning of a word or a meaning of a sentence in the natural language data cannot necessarily be represented appropriately in the semantic representation data obtained by the conventional semantic analysis by the computer as described above. As a result, a degree of accuracy for obtaining the knowledge from the natural language data is not sufficient, and reusability of the obtained knowledge is not sufficiently high.

Accordingly, it is desired to provide a method etc. of generating semantic representation data capable of representing a meaning of a word and a meaning of a sentence in natural language data more appropriately and sufficiently than ever before,

Means to Solve Problems

A first aspect according to the present invention is a semantic representation generation method of generating semantic representation data from a natural language including a content word and a function word, comprising:a preparation step of preparing a CT system table in which concept information hierarchically and ambiguously representing a meaning of a morpheme regarding a part of speech of the content word and a part of speech of the function word is recorded;a text analysis step of receiving text data described in the natural language and performing a superficial analysis including a syntax analysis on the text data, thereby generating syntax data representing a structure of a sentence included in the text data; anda semantic analysis step of generating the semantic representation data corresponding to the text data based on the syntax data, whereinthe text analysis step includes a concept tag providing step of providing a concept tag indicating the concept information for each the morpheme included in the text data based on the syntax data with reference to the CT system table,the semantic analysis step includes:a semantic tag providing step of providing a first semantic tag indicating semantic information representing a semantic relation between a first phrase/sequence of phrases and a second phrase/sequence of phrases to a first pair made up of the first phrase/sequence of phrases corresponding to a predicate and the second phrase/sequence of phrases having a modification relation with the predicate in the text data based on the syntax data; anda data generation step of generating the semantic representation data based on the concept tag provided for each the morpheme included in the text data and the first semantic tag provided to the first pair.

A second aspect of the present invention is the semantic representation generation method according to the first aspect of the present invention, whereinin the preparation step, an ST system table associating a rule for determining a second pair to which one of a plurality of second semantic tags should be provided is further prepared for each of the plurality of second semantic tags indicating plural pieces of semantic information, respectively, each representing a semantic relation between a phrase/sequence of phrases and a phrase/sequence of phrases constituting a second pair, andin the semantic tag providing step, one of the second semantic tags is provided to the second pair based on the syntax data with reference to the ST system table.

A third aspect according to the present invention is a semantic representation generation device generating semantic representation data from a natural language including a content word and a function word, comprising:a CT system storage part storing a CT system table in which concept information hierarchically and ambiguously representing a meaning of a morpheme is recorded in each of a part of speech of the content word and a part of speech of the function word for each of the part of speeches;a text analysis part receiving text data described in the natural language and performing a superficial analysis including a syntax analysis on the text data, thereby generating syntax data representing a structure of a sentence included in the text data; anda semantic analysis part the semantic representation data corresponding to the text data based on the syntax data, whereinthe text analysis part provides a concept tag indicating the concept information for each the morpheme included in the text data based on the syntax data with reference to the CT system table,the semantic analysis partprovides a first semantic tag indicating semantic information representing a semantic relation between a first phrase/sequence of phrases and a second phrase/sequence of phrases to a first pair made up of the first phrase/sequence of phrases corresponding to a predicate and the second phrase/sequence of phrases having a modification relation with the predicate in the text data based on the syntax data, andgenerates the semantic representation data based on the concept tag provided for each the morpheme included in the text data and the first semantic tag provided to the first pair.

A fourth aspect according to the present invention is a computer-readable recording medium recording a semantic representation generation program for generating semantic representation data from a natural language including a content word and a function word, whereinthe program makes a computer execute:a text analysis step of receiving text data described in the natural language and performing a superficial analysis including a syntax analysis on the text data, thereby generating syntax data representing a structure of a sentence included in the text data; anda semantic analysis step of generating the semantic representation data corresponding to the text data based on the syntax data,the computer includes a CT system storage part storing a CT system table in which concept information hierarchically and ambiguously representing a meaning of a morpheme regarding a part of speech of the content word and a part of speech of the function word is recorded,the text analysis step includes a concept tag providing step of providing a concept tag indicating the concept information for each the morpheme included in the text data based on the syntax data with reference to the CT system table,the semantic analysis step includes:a semantic tag providing step of providing a first semantic tag indicating semantic information representing a semantic relation between a first phrase/sequence of phrases and a second phrase/sequence of phrases to a first pair made up of the first phrase/sequence of phrases corresponding to a predicate and the second phrase/sequence of phrases having a modification relation with the predicate in the text data based on the syntax data; anda data generation step of generating the semantic representation data based on the concept tag provided for each the morpheme included in the text data and the first semantic tag provided to the first pair.

Another aspect of the present invention is obvious from the description of the above aspect of the present invention and an embodiment and a modification example thereof described hereinafter, thus the description thereof is omitted.

According to the above first aspect of the present invention, obtained is the semantic representation data more appropriately representing a meaning of a word included in the text data described in the natural language than ever before.

According to the above second aspect of the present invention, obtained is the semantic representation data more appropriately and sufficiently representing not only a meaning of a word but also a meaning of a sentence from the text data described in the natural language than ever before.

Both the above third aspect and the above fourth aspect of the present invention have an effect similar to the above first aspect of the present invention.

The effect of another aspect of the present invention is obvious from the description of the effect of the above aspect of the present invention and the effect of an embodiment described hereinafter, thus the description thereof is omitted.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

It is important to generate semantic representation data capable of sufficiently representing a meaning of a word and a meaning of a sentence in natural language data to increase a degree of accuracy of obtaining knowledge in establishing a knowledge base from the natural language data and achieving a question answering system in natural language. An embodiment of a device and a method for generating such semantic representation data is described hereinafter with reference to the drawings. A semantic representation generation device according to the present embodiment is typically achieved using a computer, and a semantic representation generation method according to the present embodiment is typically executed using a computer. A semantic representation generation program according to the present embodiment is used to make the computer function as the semantic representation generation device.

<1. Functional Configuration of Semantic Representation Generation Device>

FIG.1is a block drawing illustrating a functional configuration of a semantic representation generation device10according to the present embodiment. This semantic representation generation device10generates semantic representation data from natural language data (text data of a document etc. described in a natural language), and is achieved by a computer executing a semantic representation generation program described hereinafter.

As illustrated inFIG.1, the semantic representation generation device10includes a natural language analysis part110, a CT system table storage part330, and an ST system table storage part340. The CT system table storage part330stores a CT system table33. The ST system table storage part340stores an ST system table34. The natural language analysis part110includes a morphological analysis part112, a syntax analysis part114, a context analysis part116, and a semantic analysis part118. In the present embodiment, the text data as an analysis target of the semantic representation generation device10(referred to as “analysis target document” inFIG.1) is text data described in Japanese, and is stored in a text data storage part100provided outside.

In such a semantic representation generation device10, the natural language analysis part110reads the text data which is data as an analysis target and described in the natural language from the text data storage part100. In the natural language analysis part110, the morphological analysis part112firstly performs a morphological analysis on text data (referred to as “input text data” hereinafter) Din which has been read, thereby generating data (referred to as “spaced-writing data” hereinafter) D1in which the input text data is separated for each morpheme. In this the morphological analysis, a part of speech and an inflected form of a morpheme included in the spaced-writing data D1are also determined. In the morphological analysis, a concept tag (also referred to as “CT” hereinafter) is provided to each morpheme in the spaced-writing data D1with reference to the CT system table33.

The syntax analysis part114performs the syntax analysis on the spaced-writing data D1as a result of the morphological analysis, thereby generating syntax data D2representing a structure (a dependency structure and a phrase structure) for each sentence included in the input text data Din.

The context analysis part116performs the context analysis on the input text data Din based on the syntax data D2described above, thereby specifying an antecedent referenced by an anaphor included in the input text data Din, and specifies a pair of sentences having a discourse relation in the input text data Din to generate context data representing an anaphoric relation and the discourse relation in the input text data Din, and outputs context-syntax data D3made up of the context data and the syntax data D2described above. The morphological analysis part112, the syntax analysis part114, and the context analysis part116are collectively referred to as a text analysis part246in some cases hereinafter.

The semantic analysis part118provides, based on the context-syntax data D3described above, the semantic tag (also referred to as “ST” hereinafter), which indicates the semantic information representing the semantic relation between a phrase or a sequence of phrases (“the phrase or the sequence of phrases” is also referred to as “the phrase/sequence of phrases” hereinafter) and the other phrase/sequence of phrases in a pair having a modification relation in the input text data Din, to the pair thereof with reference to the ST system table34described above. The semantic analysis part118generates the semantic representation data140corresponding to the input text data Din based on the concept tag provided to each morpheme included in the syntax data D2described above and the semantic tag provided to the pair. The other type of semantic tag is provided also between a sentence and a sentence having a discourse relation, and is described hereinafter. The semantic tag indicating the semantic information representing the semantic relation between the phrase/sequence of phrases and the phrase/sequence of phrases having the modification relation is also referred to as a first semantic tag hereinafter.

<2. Hardware Configuration of Semantic Representation Generation Device>

FIG.2is a block drawing illustrating a configuration of a computer20operating as the semantic representation generation device10by a semantic representation generation program described hereinafter in the present embodiment, that is to say, a hardware configuration of the semantic representation generation device10according to the present embodiment. The computer20illustrated inFIG.2includes a CPU21, a main memory22, an auxiliary storage device23, an input operation part24, a display device25, a communication interface device26, and a recording medium reading device27. A DRAM, for example, is used for the main memory22. A hard disk or a solid state drive, for example, is used for the auxiliary storage device23. The input operation part24includes a keyboard28or a mouse29, for example. A liquid crystal display, for example, is used for the display device25. The communication interface device26is a interface circuit of a wire communication or a wireless communication. The recording medium reading device27is an interface circuit of a recording medium30storing a program, for example. A non-transient recording medium such as a CD-ROM, a DVD-ROM, or an USB memory, for example, is used for the recording medium30.

In the computer20having the above configuration, the auxiliary storage device23stores text data32as an analysis target, the CT system table33, and the ST system table34in addition to a semantic representation generation program31according to the present embodiment. The auxiliary storage device23stores the text data32, thus the text data storage part100in the semantic representation generation device10inFIG.1is achieved. The auxiliary storage device23functions as a recording medium which can be read by the computer20and stores the semantic representation generation program31. The semantic representation generation program31, the text data32, the CT system table33, and the ST system table34may be received from a server or the other computer using the communication interface device26, or may also be read from the recording medium30using the recording medium reading device27, for example.

When the semantic representation generation program31is executed in the computer20, the main memory22loads the semantic representation generation program31, and the main memory22partially or wholly loads the text data32as the input text data Din. The CPU21uses the main memory22as an operation memory, and executes the semantic representation generation program31stored in the main memory22, thereby performing a semantic representation generation process on the input text data Din stored in the main memory22. The semantic representation data140corresponding to the input text data Din is generated by this semantic representation generation process. When the CPU21performs the semantic representation generation process, the computer20functions as the semantic representation generation device10. The configuration of the computer20described above is only one example, thus the semantic representation generation device10can be achieved using various computers.

<3. CT System Table and ST System Table>

In the present embodiment, a CT system table and an ST system table described hereinafter are previously prepared, and are stored in the auxiliary storage device23as described above (FIG.2).

FIG.3is a drawing for explaining the CT system table33used in the present embodiment. The CT system table33records concept information hierarchically and ambiguously representing a meaning of a morpheme of all of part of speeches in Japanese as a natural language, that is to say, a morpheme of a content word (for example, a noun, a verb, or an adjective) included in a natural language, as well as a morpheme of a function word (for example, a postposition or an auxiliary verb) part of speech included in a natural language. As illustrated inFIG.3, it is recorded that a high-level concept of a noun “” (park) and a noun “” (school) is “name of public facility”, and a higher-level concept thereof is “space”, for example. It is also recorded that a high-level concept of a noun “” and a noun “” (company) is “name of organization”, a higher-level concept thereof is “stand-alone organizational object”, and a still higher-level concept thereof is “stand-alone object”. That is to say, as for “”, concept information hierarchically and ambiguously representing a meaning thereof is recorded. It is recorded that a concept representing a meaning of a postposition “” is “state”, “operation source”, or “causal reason”, and a high-level concept of the concept thereof is “other party”, for example. That is to say, as for the postposition “”, concept information hierarchically and ambiguously representing a meaning thereof is recorded.

FIG.4toFIG.6are drawings for explaining the ST system table34used in the present embodiment.FIG.4is a drawing for explaining a part of the ST system table34regarding a semantic tag (ST) which should be provided between a phrase and a phrase (a pair of phrases) semantically related in text data described in Japanese as natural language data.FIG.5is a drawing for explaining a part of the ST system table34regarding a semantic tag (ST) which should be provided between a sequence of phrases and a sequence of phrases or between a sequence of phrases and a phrase semantically related in text data described in Japanese.FIG.6is a drawing for explaining a part of the ST system table34regarding a semantic tag (ST) which should be provided between a sentence and a sentence (a pair of sentences) semantically related in text data described in Japanese.

The ST system table is a table associating a rule for determining a pair of phrase/sequence of phrases to which one of a plurality of semantic tags (ST) should be provided for each of the plurality of semantic tags indicating plural pieces of semantic information, respectively, each representing a semantic relation between a phrase/sequence of phrases and a phrase/sequence of phrases in Japanese as the natural language. As illustrated inFIG.4, a method of determining a target (herein, a pair of phrases) to which a semantic tag, which indicates semantic information representing a semantic relation between a phrase and a phrase, should be provided is provided as a rule of providing the semantic tag. For example, as for a semantic tag “agt” indicating semantic information of “behavior, acting subject having intention”, according to a determination method (ST provision rule) corresponding to the semantic tag “agt” in the ST system table34inFIG.4, the semantic tag “agt” is provided to “a pair of phrases in a subject-predicate relation to which a predetermined concept tag (predetermined CT) is provided to a morpheme included in those phrases”. “The predetermined concept tag (predetermined CT)” herein is selected based on the CT system table33in accordance with semantic information representing a semantic relation of a pair of phrases to which this semantic tag “agt” should be provided, specifically. The same applies to “the predetermined CT” used for defining a determination method (ST provision rule) corresponding to the other semantic tag, and an appropriate concept tag (CT) is selected based on the CT system table33in accordance with semantic information representing a semantic relation of a pair of phrases to which the semantic tag should be provided.

FIG.3toFIG.6exemplifies contents of the CT system table33and the ST system table34from a viewpoint of convenience of description of the present embodiment. The contents thereof is appropriately omitted and simplified compared with the CT system table33and the ST system table34which are actually used. Specific contents (an aspect of hierarchy of a concept of a morpheme and a provision rule of a semantic tag) of the CT system table33and the ST system table34are not also limited thereto illustrated inFIG.3toFIG.6.

As described above, the CPU21executes the semantic representation generation program31in the computer20, thus a semantic representation generation process is performed on text data of a natural language as an analysis target document.FIG.7is a flow chart illustrating a procedure of the semantic representation generation process. The semantic representation generation process includes a morphological analysis, a syntax analysis, a context analysis, and a semantic analysis.FIG.8is a flow chart illustrating a procedure of the morphological analysis,FIG.9is a flow chart illustrating a procedure of the syntax analysis,FIG.10is a flow chart illustrating a procedure of the context analysis, andFIG.11is a flow chart illustrating a procedure of the semantic analysis. In the description hereinafter, the morphological analysis, the syntax analysis, and the context analysis are also collectively referred to as “text analysis”. In the present embodiment, the CPU21executes the semantic representation generation program31, thus the computer20is operated as illustrated inFIG.7toFIG.11.

As illustrated inFIG.7, firstly, the text data32partially or wholly read from the auxiliary storage device23based on a command of an input operation etc. by a user on the input operation part24is stored in the main memory22(Step S10) as the input text data Din. The CT system table33and the ST system table34may be appropriately referenced in a step of the semantic representation generation process while being stored in the auxiliary storage device23, however, alternately, the CT system table33and the ST system table34may also be read from the auxiliary storage device23and stored in the main memory22in Step S10to increase a speed of the semantic representation generation process.

Next, the morphological analysis is performed on the input text data Din (Step S12). As illustrated inFIG.8, in the morphological analysis, a delimiter of morphemes in the input text data Din is identified to determine a part of speech of each morpheme included in the input text data Din (Step S122).

Subsequently, the concept tag (CT) is provided to each morpheme in the input text data Din with reference to the CT system table33(Step S124). As described above, recorded in the CT system table33is the concept information hierarchically and ambiguously representing the meaning of the morpheme used in the natural language (refer toFIG.3). The concept tag (CT) described above indicates the concept information. In providing the concept tag to each morpheme in the input text data Din, the concept tag falling under the morpheme is searched in order from a lowest concept (including an example) toward an upper concept in the CT system table33, and the concept tag determined to be fall under the morpheme firstly is provided to the morpheme. Provision of the concept tag to each morpheme in the input text data Din is described with reference toFIGS.12A to12D.

FIGS.12A to12Dare drawings for explaining the semantic representation generation process in the present embodiment based on specific examples. Herein, the semantic representation generation process is described with a focus on a text that the input text data Din includes a sentence (text) illustrated inFIG.12A, that is to say, “” (Taro went to the park.) This text is divided into seven morphemes as illustrated inFIG.12Bin Steps S122and S124so that the concept tag (CT) is provided to each morpheme. InFIG.12B, dotted line extending in a vertical direction show delimiters of the morphemes (the same applies toFIGS.13A to13D and14A to14Chereinafter). In the description hereinafter, data divided into morphemes, each of which is provided with a concept tag and a part of speech, as illustrated inFIG.12Bis referred to as “spaced-writing data”.

Next, the spaced-writing data D1corresponding to the input text data Din is generated based on the delimiter of the morphemes and provision of the part of speech and the concept tag to each morpheme in the input text data Din in Step S122and S124described above (Step S126). When the spaced-writing data DI is generated, the morpheme process (Step S12) is finished, and the process proceed to Step S14inFIGS.7to perform the syntax analysis.

As illustrated inFIG.9, in the syntax analysis (Step S14), based on the spaced-writing data D1, a dependency structure in the input text data Din, that is to say, a modification structure of the phrases in the input text data Din is obtained (Step S142), and a phrase structure in the input text data Din is obtained (Step S144). Herein, the phrase structure indicates a structure indicating how phrases are formed from morphemes included in the input text data Din and how a sequence of phrases is formed from the phrases, that is to say, a hierarchical structure of a sentence made up of the morpheme, the phrase, and the sequence of phrases. According to these Steps S142and S144, the dependency structure and the phrase structure are obtained as illustrated inFIG.12Cbased on the spaced-writing data D1(FIG.12B) for the text described above inFIG.12A, for example. The text inFIG.12A“” (Taro went to the park.) does not include the sequence of phrases, but is made up of three phrases (“” (Taro), “” (to the park), and “” (went)) as illustrated inFIG.12C, and these phrases have a modification relation as illustrated inFIG.12C.

Generated subsequently in this syntax analysis is the syntax data D2representing the structure (the dependency structure and the phrase structure) of each sentence included in the input text data Din based on the dependency structure and the phrase structure obtained as described above (Step S146). When the syntax data D2is generated, the syntax analysis (Step S14) is finished, and the process proceed to Step S16inFIG.7to perform the context analysis.

As illustrated inFIG.10, in the context analysis (Step S16), an anaphoric analysis is performed on the input text data Din based on the syntax data D2, thereby specifying an antecedent referenced by an anaphor included in the input text data Din (Step S162). A discourse structure analysis is performed on the input text data Din based on the syntax data D2, thereby specifying a pair of sentences having a discourse relation in the input text data Din, that is to say, a pair of sentences semantically relating to each other (Step S164). Subsequently, context data representing an anaphoric relation and a discourse relation in the input text data Din is generated based on these specified results, and the context-syntax data D3is generated from the context data and the syntax data D2described above. When the context-syntax data D3is generated, the context analysis (Step S16) is finished, and the process proceed to Step S18inFIG.7to perform the semantic analysis. Usage of the context-syntax data D3obtained by such anaphoric analysis and discourse structure analysis is described in relation to second and third generation example of the semantic representation data140described hereinafter (refer toFIGS.14A to14C,15A, and15B).

As illustrated inFIG.11, in the semantic analysis (Step S18), the semantic tag is provided between the phrase and the phrase (pair of phrases), between the sequence of phrases and the phrase (pair of sequence of phrases and phrase), and between the sequence of phrases and the sequence of phrases (pair of sequences of phrases) having a modification relation in the input text data Din based on the context-syntax data D3with reference to the ST system table34, and a third semantic tag (“second semantic tag” is described hereinafter), which indicates semantic information of a semantic relation between sentences (pair of sentences) having an anaphoric relation or the discourse relation in the input text data Din, is provided between the sentence and the sentence (pair of sentences) based on the context-syntax data D3. In the description hereinafter, the phrase, the sequence of phrases, and the sentence are collectively referred to as “the text constituent element”, and a pair of text constituent elements having the modification relation or the discourse relation are semantically related to each other.

When the semantic tag is provided to the pair of text constituent elements included in the input text data Din in Step S182, the semantic representation data140corresponding to the input text data Din is generated next based on the concept tag provided to each morpheme in the input text data Din and the semantic tag provided to the pair of text constituent elements having the semantic relation in the input text data Din (Step S184).

For example, data of a semantic representation illustrated inFIG.12Dis generated for a text “” (Taro went to the park.) illustrated inFIG.12A. In the semantic representation illustrated inFIG.12D, the phrases “”, “”, and “” as the text constituent elements are nodes, an edge is provided between the nodes semantically related to each other (between the phrases having the modification relation), the semantic tag (ST) (herein, the first semantic tag) indicating semantic information representing a semantic relation between the nodes is provided to the edge, and the concept tag (CT) indicating concept information representing each meaning is provided to each of the “”, “”, “”, “”, “”, “”. In the computer20as the semantic representation generation device10according to the present embodiment, the semantic representation data140of an appropriate data structure (a data structure appropriate for a process in a computer) corresponding to a semantic representation illustrated inFIG.12Dis generated in the main memory22, and is appropriately stored in the auxiliary storage device23, for example.

A semantic tag “lfp” indicating semantic information of “spatial terminal” representing a semantic relation between two phrases “” (to the park) and “” (went) is provided between the two phrases (refer toFIG.4). A semantic tag “agt” indicating semantic information of “behavior, acting subject having intention” representing a semantic relation between two phrases “” (Taro) and “” (went) is provided between the two phrases. However, a semantic relation represented by semantic information of “experiencer, . . . ” indicated by a semantic tag “exp” is also determined to fall under a modification relation of these phrases depending on a determination method (ST provision rule) in the ST system table34(refer toFIG.4). In this case, the two semantic tags “agt” and “exp” are provided between “” (Taro) and “” (went).

The semantic representation data140described above is generated in Step S184, the semantic analysis (Step S18) is finished. As illustrated inFIG.7, the semantic representation generation process according to the present embodiment is finished by finishing the semantic analysis.

A process by a well-known method or a publicly known method may be adopted to the specific process of the morphological analysis (FIG.8), the syntax analysis (FIG.9), and the context analysis (FIG.10) in the semantic representation generation process (FIG.7) in the present embodiment except for the provision of the concept tag based on the CT system table33(FIG.3), thus is not limited to the process described above.

<5. Generation Example of Semantic Representation Data>

FIG.13AtoFIG.13Dare drawings for explaining a first generation example of the semantic representation data140in the present embodiment. That is to say,FIG.13A to13Dare drawings for explaining what type of the semantic representation data140is generated from a text made up of one sentence “” (Taro went to cheer with Hanako.) illustrated inFIG.13Aby the semantic representation generation process (FIG.7) when such a text is included in the input text data Din.

In the present example, the text inFIG.13Ais divided into nine morphemes “”, “”, “”, “”, “”, “”, “”, “”, and “○” as illustrated inFIG.13Bby the morphological analysis (Step S12) in the semantic representation generation process, and the concept tag (CT) is provided to each morpheme. With a focus on morphemes “” and “” of a postposition in this text, concept information provided with regard to “” in the CT system table33does not hierarchically express a meaning thereof, however, concept information provided with regard to “” in the CT system table33hierarchically and ambiguously represents a meaning thereof. That is to say, as illustrated inFIG.3, with regard to the postposition “”, a concept representing a meaning thereof is recorded as “result”, “comparison standard”, “cooperative party”, “citation”, or “limitation”, and an upper concept of these concepts is recorded as “other party”. As illustrated inFIG.13B, a concept tag indicating concept information of “cooperative party-other party” is provided to the postposition “” in the present example. In a stage of the morphological analysis, with regard to a morpheme having ambiguous meanings such as the preposition “”, the plurality of concept tags may be ambiguously provided to the morpheme. However, in a case where the plurality of concept tags have meanings opposite to each other, the concept tag is provided again to the morpheme in accordance with the concept tags of morphemes before and after the morpheme in the text to be analyzed when the semantic tag is provided to the pair of text constituent elements (pair of phrases etc.) in a stage of the semantic analysis (S182inFIG.11).

Next, a dependency structure and a phrase structure of the text of the present example (FIG.13A) are obtained as illustrated inFIG.13Cby the syntax analysis (Step S14).

Subsequently, the semantic analysis (Step S18) is performed through the context analysis (Step S16). In accordance with the semantic analysis, semantic tags (herein, the first semantic tags) “agt” (or “agt, exp”), “jnt”, and “pur” are provided to the pairs of the text constituent elements semantically related to each other in the text of the present example (herein, three pairs of phrases each having a modification relation: “” (Taro) and “” (went); “” (with Hanako) and “” (went); and “” (to cheer) and “” (went)), and the semantic representation data as illustrated inFIG.13Dis generated as the semantic representation data140corresponding to the text of the present example (FIG.13A) based on the concept tag (CT) provided to each of the morphemes “”, “”, “”, “”, “”, “”, “”, and “” and the semantic tags (ST) provided to the pairs of the text constituent elements semantically related to each other. In the semantic representation data140, the semantic tag (“agt” or “agt, exp”) provided between the two phrases “” (Taro) and “” (went) is the same as the example illustrated inFIG.12D. Provided between the two phrases “” (with Hanako) and “” (went) is a semantic tag “jnt” indicating semantic information of “cooperative participant” representing a semantic relation between the two phrases based on a determination method (an ST provision rule for determining the semantic tag ST which should be provided between a phrase and a phrase) in the ST system table34inFIG.4. Provided between the two phrases “” (to cheer) and “” (went) is a semantic tag “pur” indicating semantic information of “purpose” representing a semantic relation between the two phrases.

FIG.14AtoFIG.14Care drawings for explaining a second generation example of the semantic representation data140in the present embodiment. That is to say,FIG.14A to14Care drawings for explaining what type of the semantic representation data140is generated from a text made up of two sentences “” (A heating wire was hot.) and “” (A heating wire was softened.) illustrated inFIG.14Aby the semantic representation generation process (FIG.7) when such a text is included in the input text data Din.

In the present example, in the text inFIG.14A, a first sentence “” (A heating wire was hot.) is divided into six morphemes “”, “”,“”, “”, “”, and “” and a second sentence “” (A heating wire was softened.) is divided into six morphemes “”, “”, “”, “”, “”, and “” as illustrated inFIG.14Bby the morphological analysis (Step S12) in the semantic representation generation process. The concept tag (CT) is provided to each morpheme.

Subsequently, the semantic analysis (Step S18) is performed through the syntax analysis (Step S14) and the context analysis (Step S16). In accordance with the semantic analysis, a semantic tag “gnr” is provided to the pair of the text constituent elements semantically related to each other in the text (FIG.14A) of the present example (herein, one pair of phrases having a modification relation in the first sentence (“” (A heating wire) and “” (was hot))). A semantic tag “cap” is provided to one pair of phrases (the pair of “” (A heating wire) and “” (was softened)) having a modification relation in the second sentence. The semantic representation data as illustrated inFIG.14Cis generated as the semantic representation data140corresponding to the text of the present example based on the concept tag (CT) provided to each of the morphemes “”, “”, “”, “”, “”, “”, “”, “”, “”, and “” and the semantic tag (ST) provided to the pair of the text constituent elements semantically related to each other.

In the semantic representation data140, provided between the two phrases “” (A heating wire) and “” (was hot) in the first sentence is a semantic tag “gnr” indicating semantic information of “general relation” representing a semantic relation between the two phrases based on a determination method in the ST system table34(an ST provision rule for determining the semantic tag ST which should be provided between a phrase and a phrase) illustrated inFIG.4, and provided between the two phrases “” (A heating wire) and “” (was softened) in the second sentence is a semantic tag “cap” indicating semantic information of “object causing event without intention” representing a semantic relation between the two phrases. Both the semantic tags “gnr” and “cap” provided herein are the first semantic tag. A semantic tag “eq” indicating semantic information of “equivalent” representing a semantic relation between the phrase “” (A heating wire) in the first sentence and the phrase “” in the second sentence is provided between those phrases based on the context-syntax data D3in the present example. The semantic tag “eq” provided herein does not correspond to a modification relation, and is not the first semantic tag. A semantic tag (ST) provided to a phrase/sequence of phrases in a pair having a semantic relation regardless of presence or absence of the modification relation is temporarily referred to as a second semantic tag. The second semantic tag does not depend on presence or absence of the modification relation, thus can also be considered a concept including the first semantic tag corresponding to the modification relation.

In the semantic representation data140, the second sentence “” (A heating wire was softened.) is determined to fall under “result” based on the context-syntax data D3in the present example. A semantic tag “cau” indicating semantic information representing a semantic relation (cause) between the phrase “” (was hot) corresponding to a predicative of the first sentence and the phrase “” (was softened) corresponding to a predicative of the second sentence (an edge from “” toward “”) is provided between those two phrases based on the determination result and the above semantic tags “gnr”, “cap”, and “eq” provided between the phrases in the present example. The semantic tag “cau” provided herein falls under the third semantic tag described above.

FIG.15AandFIG.15Bare drawings for explaining a third generation example of the semantic representation data140in the present embodiment. That is to say,FIG.15A and15Bare drawings for explaining what type of the semantic representation data140is generated from a text made up of three sentences illustrated inFIG.15Aby the semantic representation generation process (FIG.7) when such a text is included in the input text data Din.

In the present example, the text inFIG.15Aincludes a first sentence “” (I found a helpful book in a book store.), a second sentence “” (The book was red and cheap.), and a third sentence “” ((I) bought it immediately.). The semantic analysis (Step S18) is performed on the text through the morphological analysis (Step S12), the syntax analysis (Step S14), and the context analysis (Step S16) in the semantic representation generation process, thus the first “” (I found a helpful book in a book store.), the second sentence “” (The book was red and cheap.), and the third sentence “” ((I) bought it immediately.) are divided into morphemes (not shown in the drawings). Then, the concept tag (CT) is provided to each morpheme (refer toFIG.3), and the semantic ST is provided between the text constituent elements having the semantic relation in the text of the present example (the pair of text constituent elements), thus the semantic representation data as illustrated inFIG.15Bis generated as the semantic representation data140.

In the semantic representation data140, a semantic tag “sit” indicating semantic information of “state, condition, or case” representing a semantic relation between two phrases “” (helpful) and “” (book) is provided between the two phrases in the first sentence based on the determination method of the ST system table34(the ST provision rule for determining the semantic tag ST which should be provided between the phrases) illustrated inFIG.4. A semantic tag “obj” indicating semantic information of “object of transitive” representing a semantic relation between two phrases “” (book) and “” (found) is provided between the two phrases (corresponding to the first semantic tag). A semantic tag “loc” indicating semantic information of “spatial position” representing a semantic relation between two phrases “” (in a book store) and “” (found) is provided between the two phrases (corresponding to the first semantic tag). A semantic tag “agt” indicating semantic information of “behavior, acting subject having intention” representing a semantic relation between two phrases “” (I) and “” (found) is provided between the two phrases (corresponding to the first semantic tag). In the second sentence, a semantic tag “sit” indicating semantic information of “state, condition, or case” representing a semantic relation between two phrases “” (book) and “” (red) is provided between the two phrases (corresponding to the first semantic tag). A semantic tag “sit” indicating semantic information of “state, condition, or case” representing a semantic relation between two phrases “” (book) and “” (cheap) is also provided between the two phrases (corresponding to the first semantic tag). A semantic tag “par” indicating semantic information of “parallel relation” representing a semantic relation between two phrases “” (red) and “” (cheap) is provided between the two phrases1(corresponding to the second semantic tag). In the third sentence, a semantic tag “obj” indicating semantic information of “object of transitive” representing a semantic relation between two phrases “” (it) and “” (bought) is provided between the two phrases, and a semantic tag “tim” indicating semantic information of “temporal position” representing a semantic relation between two phrases “” (immediately) and “”(bought) is also provided between the two phrases (both corresponding to the first semantic tag). A semantic tag “eq” indicating semantic information of “equivalent” representing a semantic relation between the phrase “” (book) in the first sentence and the phrase “” (book) in the second sentence is provided between the two phrases based on the context-syntax data D3in the present example (corresponding to the second semantic tag but not corresponding to the first semantic tag). A semantic tag “corr” indicating semantic information of “anaphoric relation” representing a semantic relation between the phrase “” (it) in the third sentence and the phrase “” (book) in the second sentence is provided between the two phrases based on the context-syntax data D3in the present example (corresponding to the second semantic tag but not corresponding to the first semantic tag). A semantic tag “agt” indicating semantic information of “behavior, acting subject having intention” representing a semantic relation between the phrase “” (I) in the first sentence and the phrase “” (bought) in the third sentence is provided between the two phrases based on the context-syntax data D3in the present example (corresponding to the first semantic tag).

In the semantic representation data140, the third sentence “” ((I) bought it immediately.) is determined to fall under “result” based on the context-syntax data D3in the present example. A semantic tag “rea” indicating semantic information representing a semantic relation (cause) between the phrase “” (found) corresponding to a predicative of the first sentence and the phrase “” (bought) corresponding to a predicative of the third sentence is provided between those two phrases as illustrated inFIG.15Bbased on the determination result and the above semantic tags provided between the phrases in the present example. In the similar manner, a semantic tag “rea” indicating semantic information representing a semantic relation (cause) between the phrase “” (helpful) in the first sentence and the phrase “” (bought) in the third sentence, between the phrase “” (red) in the second sentence and the phrase “” (bought) in the third sentence, and between the phrase “” (cheap) in the second sentence and the phrase “” (bought) in the third sentence is also provided between those phrases. These semantic tags “rea” not correspond to the first semantic tag, but correspond to the second semantic tag.

According to the present embodiment described above, the concept tag (CT) is provided to not only the morpheme of the content word such as a noun or a verb but also the morpheme of the function word such as a postposition (refer toFIG.12B,FIG.13B, andFIG.14B) with reference to the CT system table33(FIG.3) recording the concept information hierarchically and ambiguously representing the meaning of the morpheme of all the part of speeches in the morphological analysis (FIG.7andFIG.8) on the input text data Din as the natural language data, and the semantic representation data140is generated (for example,FIG.12D,FIG.13D, andFIG.14C) based on the provision of such a concept tag to the morpheme in the semantic analysis (FIG.7andFIG.11). Accordingly, obtained is the semantic representation data140more appropriately representing a meaning of a word included in the natural language data than ever before.

According to the present embodiment, the semantic analysis (FIG.11) is performed on the input text data Din as the natural language data through the syntax analysis (FIG.9) and the context analysis (FIG.10) after the morphological analysis (FIG.8) including the provision of the concept tag to the morpheme described above. In the semantic analysis, the semantic tag (ST) is provided to each of the pair of text constituent elements (the phrase, the sequence of phrases, or the sentence) semantically related in the input text data Din with reference to the ST system table34, and the semantic representation data140is generated (for example,FIG.12D,FIG.13D, andFIG.14C) based on the provision of the concept tag to the morpheme described above and the provision of the semantic tag to the pair of such text constituent elements. In the ST system table34referenced at this time, as illustrated inFIG.4, the ST provision rule (the method for determining the type of the pair of text constituent elements to which the semantic tag should be provided) is provided to each of the plurality of semantic tags each illustrating the plural pieces of semantic information representing the semantic relation of the pair of text constituent elements (the phrase, the sequence of phrases, or the sentence) semantically related in the natural language regardless of whether or not the text constituent element of the phrase/sequence of phrases corresponding to the predicative. Thus, according to the present embodiment, obtained is the semantic representation data140representing not only a meaning of a word but also a meaning of a phrase and a meaning of a sentence from natural language data more appropriately and sufficiently than ever before. The ST provision rule provided in the ST system table34includes a rule defining the pair of text constituent elements to which the semantic tag should be provided using the concept tag (CT) of the morpheme included in the pair thereof. According to such an ST provision rule, the semantic tag which should be provided to the pair of text constituent elements can be determined more accurately.

When the semantic representation data140generated by such a present embodiment is used for obtaining knowledge from a natural language data and a question answering system by a natural language, a degree of accuracy of obtaining the knowledge and reusability of the obtained knowledge can be increased.

The present invention is not limited to the embodiment described above, however, various modification can be performed within a scope of the present invention,

For example, in the embodiment described above, the input text data Din for generating the semantic representation data140is the text data described in Japanese. However, the semantic representation data140can be generated from text data of the other natural language such as the input text data Din as text data described in English, for example, by a semantic representation generation device or a semantic representation generation method made up in a manner similar to the semantic representation generation device or the semantic representation generation method according to the embodiment described above.

In the CT system table33used in the embodiment described above, as illustrated inFIG.3, the concept representing the meaning of the morpheme in the natural language is hierarchically classified, however, the hierarchical classification illustrated inFIG.3is illustrative, thus the concept representing the meaning of the morpheme in the natural language may be hierarchically classified by the other aspect. The ST provision rule corresponding to each semantic tag in the ST system table34used in the embodiment described above may be defined in a form different from the ST provision rule illustrated inFIG.4toFIG.6. Furthermore, in the ST system table34used in the embodiment described above, the ST provision rule is provided to the pair of text constituent elements (the phrase, the sequence of phrases, or the sentence) semantically related in the natural language regardless of whether or not the text constituent clement as the phrase corresponding to the predicative, however, it is alternatively applicable that the ST provision rule is provided to the pair of text constituent elements only when one of the pair of text constituent elements semantically related in the natural language is a phrase corresponding to the predictive as with a conventional provision of a deep case etc.