Patent Grant
7979265
This application is based upon and claims the benefit of priority from prior Japanese Patent Application No. 2004-319708, filed Nov. 2, 2004, the entire contents of which are incorporated herein by reference.
1. Field of the Invention
The present invention relates to a machine translation system, method and program.
2. Description of the Related Art
In a machine translation system in which sentences written in a first language (source language) are translated into those written in a second language (target language) using a computer, firstly, an input sentence is divided into predetermined translation units (such as words and phrases) by morpheme analysis or sentence structure analysis. Subsequently, a translation dictionary is searched in units of processing to determine a translation rule to be applied, thereby determining corresponding translation words (phrase). The determined words (phrases) are connected in accordance with a predetermined translation rule, thereby acquiring a translation corresponding to the input sentence.
In a document having a structure, such as a table structure or enumerated structure in which words or sentences are arranged in order, if the table structure or enumerated structure is translated, a word or sentence in each cell of the table, or an enumerated word or sentence is extracted and input for translation to such a machine translation system as the above.
Sentences, such as sentences in cells of a table, or enumerated ones, which are regularly arranged as structural elements, may well have no grammatically correct structures or may well be very short. Accordingly, the above-mentioned method, in which sentences are extracted one by one from a table structure or enumerated structure and subjected to machine translation, does not provide much information that can be used as a key to analysis for translation or to selection of one from possible translations, resulting in degradation of translation accuracy.
When translating standard sentences, co-occurrence information in a sentence or in context is utilized (see, for example, Jpn. Pat. Appln. KOKAI Publication No. 3-175573). However, if this method is directly applied to translation of data of a table structure or enumerated structure, it is difficult to output stable translation results since the manner of co-occurrence may vary depending upon the arrangement of sentences in the structure.
Specifically, in the case of, for example, an enumerated structure of Japanese characters
and
belong to a category of “order”. Therefore, it is desirable that Japanese characters
and
be translated into numerals (1), (2) and (3), or alphabets (a), (b) and (c), respectively.
are Japanese words.
means Monday (Getsu) or moon (Tsuki),
means Tuesday (Ka) or fire (Hi), and
means Wednesday (Sui) or water (Mizu). There is a similar enumerated structure of
In this case, Japanese characters
and
also belong to the category of “order”. In this case, it is desirable that Japanese characters
and
be translated into numerals (1), (2) and (3), or alphabets (a), (b) and (c), respectively. Thus, in the former case, Japanese character
should be translated into (1) or (a), whereas in the latter case, the same Japanese character
should be translated into (2) or (b). Moreover, there may be even a case where enumerated structures are included in a nested structure, in which two or more ambiguous characters, such as the above-mentioned character
may well appear. In the conventional translation using co-occurrence information, translation cannot be performed in consideration of the rule of an enumerated structure, under which the characters are arranged regularly. Accordingly, characters, which are ambiguous like the above-mentioned character
may be translated wrongly, which degrades the quality of translation. Further, the set of Japanese characters (words)
(Getsu),
(Ka),
(Sui) . . . included in the enumerated structure indicates that these words actually belong to a category of “a day of the week”. Accordingly, these words should be translated into “Monday”, “Tuesday”, “Wednesday”, . . . , respectively. However, Japanese words
also mean “moon”, “fire”, “water”, . . . , respectively. In the prior art translation technique in which translation cannot be performed in consideration of the rule of such an enumerated structure as the above, under which the words are arranged regularly, words
. . . may well be translated into such standard (default) translation words as “moon”, “fire”, “water”, . . . , respectively.
The same can be said of a table structure. Specifically, when in the table structure, cells that store Japanese words exist at the index rows (columns) of each column (row), these words should be translated into “Monday”, “Tuesday”, “Wednesday”, . . . since the set of words
indicates that these words belong to the category of “day of the week”. In the prior art technique, however, words
may well be translated into such standard (default) translation words as “moon”, “fire”, “water”, . . . , respectively, as in the case of the enumerated structure, because translation cannot be performed in consideration of the rule of the row (column) direction of the table structure, under which the words are arranged regularly. If the translation method utilizing co-occurrence information is applied to the translation of a table, it is possible that the translation of a certain cell may be influenced by other cells in the table having a low degree of relationship with respect to the certain cell (e.g., the cells located obliquely above or below). Thus, stable translation is still difficult.
As described above, the prior art techniques cannot accurately translate a document having a table structure or enumerated structure in which words or sentences are regularly arranged.
In accordance with a first aspect of the invention, there is provided a machine translation system comprising: a storage unit configured to store a plurality of interpretation data items each including a first expression element representing a first expression of a first language, a word set element including the first expression and a second expression element representing a second expression of a second language, the first expression being to be translated into the second expression, the first expression and the second expression belonging to a category; an acquiring unit configured to acquire a predetermined expression of the first language, the predetermined expression being contained in a to-be-translated text having a structure; a detecting unit configured to detect, from the interpretation data items, a plurality of interpretation data items each including an expression element representing the predetermined expression; a selection unit configured to select, from the detected interpretation data items, an interpretation data item including a word set element corresponding to a same attribute or an equivalent positional relationship in the structure; and a translation unit configured to translate the predetermined expression into the second language, using the selected interpretation data item.
According to a second aspect of the invention, there is provided a machine translation method comprising: preparing a storage unit configured to store a plurality of interpretation data items each including a first expression element representing a first expression of a first language, a word set element including the first expression and a second expression element representing a second expression of a second language, the first expression being to be translated into the second expression, the first expression and the second expression belonging to a category; acquiring a predetermined expression of the first language, the predetermined expression being contained in a to-be-translated text having a structure; detecting, from the interpretation data items, a plurality of interpretation data items each including an expression element representing the predetermined expression; selecting, from the detected interpretation data items, an interpretation data item including a word set element corresponding to a same attribute or an equivalent positional relationship in the structure; and translating the predetermined expression into the second language, using the selected interpretation data item.
According to a third aspect of the invention, there is provided a program stored in a computer readable recording medium, comprising: means for instructing a computer to access a storage unit configured to store a plurality of interpretation data items each including a first expression element representing a first expression of a first language, a word set element including the first expression and a second expression element representing a second expression of a second language, the first expression being to be translated into the second expression, the first expression and the second expression belonging to a category; means for instructing the computer to acquire a predetermined expression of the first language, the predetermined expression being contained in a to-be-translated text having a structure; means for instructing the computer to detect, from the interpretation data items, a plurality of interpretation data items each including an expression element representing the predetermined expression; means for instructing the computer to select, from the detected interpretation data items, an interpretation data item including a word set element corresponding to a same attribute or an equivalent positional relationship in the structure; and means for instructing the computer to translate the predetermined expression into the second language, using the selected interpretation data item.
An embodiment of the invention will be described in detail with reference to the accompanying drawings.
The embodiment of the invention has been developed in light of the above-described problems, and aims to provide a machine translation system for highly accurately and reliably translating a document having a table structure or enumerated structure. It also aims to provide a machine translation method and program used in the machine translation system.
The embodiment of the invention can highly accurately and reliably translate a document having a table structure or enumerated structure.
As shown, an input unit 1 acquires the electronic data (hereinafter referred to simply as “a document”) of a document (original document) as a translation target, and output it to a controller 2. The input unit 1 is formed of, for example, a keyboard, mouse, etc., and is operated by a user to input a document. The input unit 1 may be formed of a reading unit for reading data from a computer-readable medium, such as an optical character reader (OCR), floppy disk, magnetic tape, magnetic disk or optical disk. The input unit 1 supplies the thus-read document to the controller 2. Further, the input unit 1 is used to supply various commands to the controller 2.
An output unit 3 outputs a translation result supplied from the controller 2. Further, the output unit 3 displays, for example, the translation result on the screen of a display unit (not shown) under the control of the controller 2. The output unit 3 outputs the translation result in the form of sound, and displays a response made by the controller 2 in reply to a command.
The output unit 3 may be formed of, as well as various types of display units, a printing unit, or an output unit for outputting data to a computer-readable medium, such as an optical character reader (OCR), floppy disk, magnetic tape, magnetic disk or optical disk, or a transmission unit for transmitting a document to another medium.
A translation unit 4 performs, under the control of the controller 2, translation processing on an original document input from the input unit 1, using a translation dictionary 5 described later, and outputs the translation result to the controller 2.
The translation dictionary 5 stores a vocabulary dictionary (not shown), and various rules necessary for translation processing. For instance, the translation dictionary 5 stores a word/idiom dictionary, morpheme analysis rules, structure/meaning analysis rules, translation conversion rules, translation generation rules, etc.
The controller 2 detects, in a to-be-translated document, a structure in which words or sentences (expression elements) are arranged regularly, such as a table structure, enumerated structure, or hierarchical structure such as a chapter or section. Specifically, when detecting a chapter or section in a to-be-translated document, a character string, which includes a character string (e.g., “The first chapter”, “The second chapter”, . . . , “Ch”, “Sec”, etc.) generally included in the title sentence of a chapter or section, is determined to be the title sentence of a chapter or section. Thus, the chapter or section is detected. Similarly, when detecting a table structure, ruled-line data is detected in a document, and it is determined whether the ruled lines define a rectangle, and whether the rectangle contains areas divided by other ruled lines. In the case of an enumerated structure, it can be detected based on an indent or a symbol attached at the top of each sentence. Further, if a to-be-translated document is a structured document with tags, such as HTML or XML, it can be easily detected by detecting the tags.
When a structure, such as a table structure or hierarchical structure, is detected in a to-be-translated document, the controller 2 transmits, to a document structure analysis unit 6, the entire detected structure included in the to-be-translated document. The document structure analysis unit 6 analyzes the structure, and extracts, from the structure, a set of words, phrases or sentences (expression elements) (hereinafter referred to simply as “a word set element (expression set element)”) arranged as the components of the structure.
An intra-structure data storing unit 7 stores the word set element included in the detected structure and extracted by the document structure analysis unit 6. The stored data is erased at an appropriate time, e.g., when the detected structure has been entirely translated.
If the to-be-translated structure is a nested structure, the document structure analysis unit 6 may search the entire structure and store all word set elements in the intra-structure data storing unit 7, or may perform, for example, structure analysis in a feedback manner to realize processing in units of small structures that are included in the structure. In this case, if the intra-structure data storing unit 7 utilizes a data structure such as a stack structure, the word set elements contained in the structure can be stored in a feedback manner. Erasure of stored data may be performed each when structure analysis processing performed in a feedback manner is finished.
An intra-structure interpretation database 8 stores a plurality of interpretation data items. These data items include data indicating words or sentences in a structure that may easily be wrongly interpreted, and word set elements belonging to the same domains as the to-be-translated words or sentences. The data items further indicate techniques as to how the to-be-translated words or sentences should be translated (interpreted) if the word set elements, which belong to the same domains as the to-be-translated words or sentences, have the same attributes as the to-be-translated words or sentences or have an equivalent relationship in structure to that of the to-be-translated words or sentences. Each interpretation data item may further include a condition under which matching with the word set element included therein is performed. The condition indicates, for example, a minimum number of matching occasions, a limit to the order of appearance of the words included in the word set element, or a determination as to whether perfect matching is required or imperfect matching is allowable.
During translation, the translation unit 4 inquires of the intra-structure interpretation database 8 whether the database 8 contains interpretation data applicable to each word set element stored in the intra-structure data storing unit 7. In this case, when the interpretation data contains the above-described condition, the intra-structure interpretation database 8 determines, using the condition, whether the word set elements in the structure are identical to those in the interpretation data. If there is no applicable interpretation data, this is reported to the translation unit 4, and standard translation processing is performed. If applicable interpretation data is detected, it is sent to the translation unit 4, where interpretation concerning each to-be-translated word is replaced with that of the interpretation data, thereby performing translation processing.
Referring now to the flowchart of
When document data having the table structure shown in
The document structure analysis unit 6 analyzes the received table-structure data. In this case, it is determined that the data is the table of seven rows and three columns shown in
The intra-structure data storing unit 7 stores the table data extracted by the document structure analysis unit 6. At this time, the table data is stored so that its column data and row data can be discriminated. For instance, the data may be stored in a one-dimensional form so that the relationship between the words and cells contained in the table of seven rows and three columns will be clear.
The controller 2 reads the words one by one from the cells of the table shown in
Assume here that the original word is Japanese word “” contained in the cell of the first column and second row in the table of
” may be sent as the original word. Alternatively, morpheme analysis may be performed on it, and the word and the analysis result be sent. Similarly, a structure analysis result, meaning analysis result, conversion result and/or generation result may be sent together with the word. Further, data (2, 1) is sent as coordinate data, since the original word is positioned at the second row and first column.
Upon receiving, from the translation unit 4, information concerning the to-be-translated original document data, and the coordinate information, the intra-structure interpretation database 8 accesses the intra-structure data storing unit 7 to acquire, based on the received coordinate information, a word set element arranged in the same column or row as that in the presently processed table (step S4). In this case, since the coordinates are (2, 1), the word set element corresponding to the cells of the second row, and the word set element corresponding to the cells of the first column are acquired from the data shown in (Gutsu),
(Tsuki),
(Kanto, means the Kanto region, which is the name of a certain district in Japan)” (word set element A). Similarly, the word set element in the first column is “Day of the week,
(Getsu, means Monday),
(Ka, means Tuesday),
(Sui, means Wednesday),
(Moku, means Thursday),
(Kin, means Friday),
(Do, means Saturday)” (word set element B).
After acquiring word set elements A and B from the. intra-structure data storing unit 7, the intra-structure interpretation database 8 searches for interpretation data based on the original document data acquired from the translation unit 4. Using, for example, headword as a key, the interpretation data shown in
Noun; Monday;
Noun; Moon;
The two interpretation data items contain respective word set elements (Getsu),
(Ka),
(Sui),
(Moku),
(Kin),
(Do),
(Nichi)” (word set element C) and “
(Tsuki, means moon),
(Chikyu, means the earth),
(Kasei, means Mars),
(Suisei, means Mercury),
(Mokusei, means Jupiter),
(Kinsei, means Venus),
(Dosei, means Saturn),
(Tasiyou, means the Sun)” (word set element D). At this time, matching is performed between word set elements C and D, and word set elements A and B acquired from the original document data and stored in the intra-structure data storing unit 7 (step S6). For this matching, the above-described condition can be utilized effectively. For instance, to determine whether a word set element included in certain original document data is identical to a word set element included in certain interpretation data, the condition that k words or more included in the word set element of the original document data must be identical to those of the interpretation data may be employed. Alternatively, the condition that all words of the word set element must be identical between the original document data and the interpretation data may be employed. Similarly, the condition that only part of the word set element is identical between the original document data and the interpretation data may be employed. Further, the condition that all words of the word set element and even the order of arrangement of the words must be identical between the original document data and the interpretation data may be employed. Appropriate use of these conditions enables the matching process to be controlled freely.
In the above-mentioned case, the intra-structure interpretation database 8 determines that word set elements B and C are identical to each other, since the degree of matching therebetween is high. Accordingly, the former interpretation data item, i.e., Noun; Monday;
is selected (step S7). As is evident from the interpretation data items, each interpretation data item contains information, such as “part of speech” and “translation word”, necessary for translation. In the case of the selected interpretation data item, “Monday” is selected as a translation of Japanese word
in the cell of the first column and second row in the table of
Upon receiving the interpretation result from the intra-structure interpretation database 8, the translation unit 4 performs translation processing based on the received interpretation result, and sends the translation result, i.e., the translation word “Monday”, to the controller 2.
Upon receiving the translation result, the controller 2 sends it to the output unit 3 (step S8).
Thus, the machine translation system of in the cell of the second column and second row in the table of
positioned at the fifth row and third column of the table of
can be correctly translated into “Chugoku” (which is the name of a certain district in Japan) instead of, for example, “China”, referring to the words in the third column of the table of
itself.
When a translation target has an enumerated structure or chapter/section structure, it is sufficient if in the above-described process flowchart, the titles of chapters and the titles of sections are set as words contained in n rows of first-column cells, and the same process as the above performed for each cell of the table structure is performed. Concerning other types of hierarchical structures, correct translation can be realized by appropriately modifying the above process.
As described above, in the embodiment, when document data having a structure, such as a table structure (as shown, for example, in that is positioned at the second row and first column of
Referring to the flowchart of
When English document data having the table structure shown in
The document structure analysis unit 6 analyzes the received table-structure data. In this case, it is determined that the data is a table that has three rows, and six columns in the first row, seven columns in the second row, and six columns in the third row, as is shown in
The intra-structure data storing unit 7 stores the table data extracted by the document structure analysis unit 6.
The controller 2 reads the words (or phrases) one by one from the cells of the table shown in
Assume here that the original phrase is “Speaker Info” contained in the cell of the first row and fifth column in the table of
Upon receiving, from the translation unit 4, information concerning the to-be-translated original document data, and the coordinate information, the intra-structure interpretation database 8 accesses the intra-structure data storing unit 7 to acquire, based on the received coordinate information, a word set element arranged in the same column or row as that in the presently processed table (step S4). In this case, since the coordinates are (1, 5), the word set element corresponding to the cells of the first row, and the word set element corresponding to the cells of the fifth column are acquired from the data shown in
After acquiring word set elements A′ and B′ from the intra-structure data storing unit 7, the intra-structure interpretation database 8 searches for interpretation data based on the original document data acquired from the translation unit 4. Using, for example, headword “Speaker” as a key, the interpretation data shown in
“Speaker Info; noun; Overview/Agenda/Registration/Participants List/Speaker Info/Fees Info”
“Speaker; noun;
CPU/Chipset/Display/Drive/HDD/Memory/OS”
The two interpretation data items contain respective word set elements “Overview, Agenda, Registration, Participants List, Speaker Info, Fees Info” (word set element C′) and “CPU, Chipset, Display, Drive, HDD, Memory, OS” (word set element D′). At this time, matching is performed between word set elements C′ and D′, and word set elements A′ and B′ acquired from the original document data and stored in the intra-structure data storing unit 7 (step S6). For this matching, the above-described condition can be utilized effectively. For instance, to determine whether a word set element included in certain original document data is identical to a word set element included in certain interpretation data, the condition that k words or more included in the word set element of the original document data must be identical to those of the interpretation data may be employed. Alternatively, the condition that all words of the word set element must be identical between the original document data and the interpretation data may be employed. Similarly, the condition that only part of the word set element is identical between the original document data and the interpretation data may be employed. Further, the condition that all words of the word set element and even the order of arrangement of the words must be identical between the original document data and the interpretation data may be employed. Appropriate use of these conditions enables the matching process to be controlled freely.
In the above-mentioned case, the intra-structure interpretation database 8 determines that word set elements A′ and C′ are identical to each other, since the degree of matching therebetween is high. Accordingly, the former interpretation data item, i.e., “Speaker Info; noun; Overview/Agenda/Registration/Participants List/Speaker Info/Fees Info”, is selected (step S7). As is evident from the interpretation data items, each interpretation data item contains information, such as “part of speech” and “translation word”, necessary for translation. In the case of the selected interpretation data item, Japanese word
(Kouensha-Jouhou)” is selected as a translation of “Speaker Info” in the first row, fifth column cell of the table structure of the original document data, and is sent to the translation unit 4.
Upon receiving the interpretation result from the intra-structure interpretation database 8, the translation unit 4 performs translation processing based on the received interpretation result, and sends the translation result, i.e., the translation word , to the controller 2.
Upon receiving the translation result, the controller 2 sends it to the output unit 3 (step S8).
Thus, the machine translation system of (Supiikaa)”. Namely, the same words existing in the same table but having different meanings can be correctly translated into different words corresponding to the respective meanings, by referring to each word in each column. Further, the word “Contact” in the third row, sixth column cell of the table of
(Otoiawase)” (instead of, for example, Japanese word
(Sesshoku)” different from the former), by referring to the words in the cells of the third row.
As described above, in the embodiment, when document data having a structure, such as a table structure (as shown, for example, in
As described above, in the embodiment, document data having a structure, such as a table structure, enumerated structure or chapter/section structure, can be translated in light of attributes in structure, the hierarchical and/or positional relationship in structure, etc. As a result, further accurate and reliable translation results can be acquired.
In addition, the operations performed by the machine translation system of
Additional advantages and modifications will readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described herein. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents.
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