JPH07141381A - Electronic dictionary display device - Google Patents

Abstract

PURPOSE:To display contents in accordance with the order of semantic distance to a retrieval keyword by using a thesaurus for the retrieval keyword inputted in a natural language. CONSTITUTION:An input means 1 is the keyboard to input an inquiry text including a marked keyword. A storage means 2 is the floppy disk/ROM or the like where the thesaurus is stored. A temporary storage means 3 temporarily stores inputted information in a RAM, a hard disk, a floppy disk, or the like. An input character string analysis means 4 subjects the inquiry text to morpheme analysis and syntax analysis to segment a word for thesaurus collation. A data exchanging means 5 sends the keyword to a dictionary retrieval device and receives dictionary information corresponding to the keyword from the dictionary retrieval device.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an electronic dictionary display device, and more particularly, to an inquiry including a marked entry word by using a thesaurus prepared in advance in displaying search contents of an electronic dictionary. The present invention relates to an electronic dictionary display device that rearranges and displays dictionary information in a word sense order similar to a sentence. For example, electronic dictionary, database system,
It is applied to information recall systems.

[0002]

2. Description of the Related Art As a known document describing a conventional electronic dictionary display device, for example, Japanese Patent Laid-Open No. 55-13186.
There is No. 9 publication. According to this publication, when a word is input by operating a character input key and a storage means for storing a plurality of sets of a word and information related to the word, the word is associated with the input word based on the input content. The information to be output is output.

[0003] When looking up a translated word of a word whose meaning is unknown not only in the electronic dictionary system but also in a paper dictionary, the dictionary information divided by the word meaning is searched from the beginning. Here, the word meaning is a paragraph / sentence composed of a translated word, an example, a usage, a usage, a phrase, a etymology, etc., which is divided by an Arabic numeral or an alphabet. For this reason, when there are many word meanings and examples such as verbs, the dictionary information displayed on the display is small in the electronic dictionary system, so that it takes a considerable amount of labor to look up the translated word.

For example, the meaning of the word "meet" in the phrase "meet our request" is "accept / satisfy". In commonly-used dictionaries, the meaning of "meet" is about 7,8 transitive verbs only (translations are about 20), and the meaning of "respond" is the last or the previous meaning. It is listed in the section. For this reason, in the case of an electronic dictionary that displays all dictionary information, in order to find the translated word "accept / satisfy", scroll around tens of sentences including other meanings / translations / examples while looking at the screen. There is a need to. Further, even in an electronic dictionary that displays only translated words, it is necessary to scroll the screen of the display for several lines, although it is not necessary to scroll that much.

On the other hand, some electronic dictionaries are provided with a conditional search for searching an example using a plurality of search keywords, but since the matching of the keyword and the index is a perfect match, it is unnecessary. I will also search for example sentences. For example, “meet the deman” is a common example.
"d of the age" (according to the demands of the age) is registered in the dictionary, but since the matching is a perfect match between the search keyword and the index, in the case of the previous phrase "meet our request", "meet", " Even if our ”and“ request ”are used as search keywords, they only match the index“ meet ”.

Therefore, all the example sentences matching "meet" are displayed. From this, “meet our request”
In comparison, finding the closest “meet the demand of the age” is also a lot of work. Even if you can find an example sentence that is close to the meaning well, if the translated word of the example sentence is not appropriate, search the translated word in the meaning of the example sentence, the example sentence, the usage, etc. with the entry word, and display the dictionary information. It is necessary to search again from inside.

[0007]

As described above, the word meanings of the dictionary information in the conventional electronic dictionary display device are arranged in descending order of frequency of use. However, the meaning of the word desired by the user is often infrequently used. This is because the meaning of frequently used words is remembered by the user, and it is not necessary to look at the dictionary.

In the current electronic dictionaries, although the display screen is small, the dictionary information is to be displayed faithfully. Therefore, it is necessary to see the word meanings arranged in descending order of frequency of use. The amount of information is insufficient for a search keyword including only headwords, and it is difficult to specify which word meaning the user wants to search.

However, the word that the user wants to look up is
It is taken from a sentence that the user intends to translate or want to know the meaning, and the phrase / clause information of the sentence can be used as a clue for searching the meaning of the word that the user wants to know. As one of the search methods using the phrase / clause information of the sentence, there is a conditional search of an example using a plurality of search keywords. However, in the search by the conditional search, a large number of example sentences are searched. For this reason, it is troublesome to look up an appropriate translated word, and it is necessary to check the word meaning information included in the example sentence again in some cases.

The present invention has been made in view of such a situation, and in displaying the retrieval contents of the electronic dictionary, the meaning of the inquiry sentence including the marked headword is utilized by using a thesaurus prepared in advance. It is an object of the present invention to provide an electronic dictionary display device in which dictionary information is rearranged and displayed in units of word meanings so that the word meanings closer to each other are displayed first.

[0011]

In order to achieve the above object, the present invention provides (1) input means for inputting an inquiry sentence including a marked headword, storage means for storing a thesaurus, and processing data. Temporary storage means for temporarily storing intermediate results and intermediate results of control, input character string analysis means for extracting words for thesaurus matching from the inquiry sentence, and sending headwords in the inquiry sentence to an external dictionary search device for retrieval Data exchange means for receiving dictionary information corresponding to the identified headwords, example sentence word extraction means for extracting words for thesaurus matching from example sentences in the dictionary, and similarity between thesauri matching words extracted using the thesaurus A similarity calculation means for calculating, a sorting means for sorting the dictionary information in descending order of similarity, and a display means for displaying the sorted dictionary information. And a control means for controlling each of the above means, wherein the thesaurus is used to rearrange and display the dictionary information so that the semantics close to the inquiry sentence are displayed first, and (2) It is characterized in that the example sentences in the dictionary information are rearranged and displayed in descending order of similarity.

[0012]

The control means causes the thesaurus to be stored in the temporary storage means by the input means from the storage means such as a floppy disk or ROM. Further, the inquiry sentence including the marked headword is input by the input unit and stored in the temporary storage unit. Next, morphological analysis / syntactic analysis of the inquiry sentence including the marked headword,
The thesaurus matching word is cut out and stored in the temporary storage means. Next, the dictionary information corresponding to the entry word included in the inquiry sentence is searched by the dictionary search device connected to this device. The dictionary retrieval device retrieves the dictionary information corresponding to the entry word from the electronic dictionary for the entry word. Then, the dictionary information of the searched entry word is stored in the temporary storage means. Next, the example sentence word extracting unit morphologically and syntactically analyzes the example sentence in the dictionary information, and cuts out a word for thesaurus matching from the example sentence. The cut out words are stored in the temporary storage means. Next, the thesaurus is used to compare the inquiry sentence stored by the similarity calculating unit with the words cut out from the example sentence for thesaurus matching, and the similarity is calculated. Next, the dictionary information is rearranged in word sense units in descending order of similarity calculated by the rearrangement means. Alternatively, the example sentences in the dictionary information are rearranged in the descending order of similarity, and the other than the example sentences are deleted. Next, the sorted dictionary information is displayed by the display means.

[0013]

Embodiments will be described below with reference to the drawings. FIG. 1 is a block diagram of an electronic dictionary display device according to the present invention, in which 1 is an input means, 2 is a storage means, 3 is a temporary storage means, 4 is an input character string analysis means, 5 is a data exchange means,
Reference numeral 6 is an example sentence word extracting unit, 7 is a similarity calculating unit, 8 is a rearranging unit, 9 is a displaying unit, and 10 is a controlling unit.

The input means 1 is a keyboard or the like for inputting an inquiry sentence including a marked headword. The storage means 2 is a floppy disk / ROM or the like for storing the thesaurus. The temporary storage means 3 temporarily stores the input information in a RAM, a hard disk, a floppy disk, or the like. The input character string analysis unit 4 morphologically / syntactically analyzes the inquiry sentence and cuts out a word for thesaurus matching. The data exchange means 5 sends the entry word to the dictionary search device, and receives the dictionary information corresponding to the entry word from the dictionary search device.

The example sentence word extracting means 6 morphologically and syntactically analyzes the example sentence in the dictionary information, and cuts out a word for thesaurus matching from the example sentence. The similarity calculation means 7 matches the words cut out from the inquiry sentence with the words cut out from the example sentence using a thesaurus to calculate the similarity on the thesaurus. The rearrangement means 8 rearranges the dictionary information in descending order of similarity. The display means 9 is a CRT (Cathode RayTube) or the like for displaying the input inquiry text and the sorted dictionary information as character information. The control means 10 is a CP that controls each of the above means.
U (central processing unit).

FIG. 2 is a diagram showing functional modules for explaining the operation of the electronic dictionary display device according to the present invention.
In the figure, 11 is a data preprocessing module, 12 is an input character string analysis module, 13 is a data conversion module, and 14 is a data conversion module.
Is an example sentence word cutout module, 15 is a similarity calculation module, 16 is a rearrangement module, and 17 is a display module.

The data preprocessing module 11 inputs the inquiry sentence including the thesaurus reading and the marked entry word. Input character string analysis module 12
Cuts out words for query sentence thesaurus matching.
The data exchange module 13 sends the entry word included in the inquiry sentence to the dictionary search device, and receives the dictionary information corresponding to the entry word searched by the dictionary search device. The example sentence word slicing module 14 morphologically and syntactically analyzes the example sentence in the dictionary information corresponding to the entry word, and slices a word for thesaurus matching. The similarity calculation module 15 digitizes the similarity between the words cut out from the inquiry sentence and the words cut out from the example sentence using a thesaurus. The rearrangement module 16 rearranges the dictionary information in descending order of similarity based on the calculation result. Display module 1
7 displays the sorted dictionary information. Hereinafter, each of these modules will be specifically described.

The data preprocessing module 11 stores the thesaurus previously stored in the storage means in the temporary storage means.
Also, it supports the input of the inquiry sentence including the marked headword and stores it in the temporary storage means. FIG. 3 shows a part of a thesaurus created from a commercially available thesaurus in a tree structure. The end of the thesaurus is a word name that appears specifically in the dictionary information, and the intermediate node expresses an intermediate concept. Also, the top node is an empty node.

The inquiry sentence is input by a character string expressed in natural language. However, you may input one word at a time. However, in that case, the user needs to specify which word is the headword. The following is an example of inputting a query containing the marked headword. To specify the entry word, use a special symbol after the word!
Any method can be used as long as the apparatus can identify a keyword serving as a headword, such as adding. After that, “meet o
It will be explained based on the search example of "ur request".

Input example: meet! In our request input character string analysis module 12, the headword is identified by the mark for designating the headword, and “meet” is cut out. Then, the morphological analysis / syntactic analysis of the inquiry sentence is performed,
The words of nouns for thesaurus matching other than headwords are cut out. Word extraction is performed according to the following rules.

When the headword is a transitive verb and the input character string is a sentence / phrase / section, the noun that is the object of the transitive verb is cut out. Cutout ・ If the entry word is a noun and the input character string is a phrase / clause, and if the preposition is adjacent to the noun phrase containing the entry word, the noun or preposition of the noun phrase adjacent to that preposition is not adjacent, If there is a noun that is directly or adornment modified to the headword, cut out that noun.-If the headword is an adjective, cut out the noun to which the adjective is applied.-The headword is a be verb, article, prefix, or suffix. Rhyme, conjunction,
In the case of auxiliary verbs, interjections, pronouns, prepositions, adverbs, and relatives, words are not cut out for matching.

In this example "meet! Our request", "mee!
"t" is a transitive verb and its object is the word "request"
Will be cut out. If the cut out noun has a plural form, it is converted to a singular form, and if it is a personal noun to be a third person, it is converted to a first person. This is to reduce the capacity of the thesaurus. When the input headword changes like "met", its original form is used as the headword. Similarly, in the case of plural nouns, the singular form is basically used, and in the case of adjectives, the original form is the headword.

The data exchange module 13 sends the input headword "meet" to the dictionary search device and receives dictionary information (FIG. 4) corresponding to the headword searched by the dictionary search device. In FIG. 4, only the word meanings and example sentences are used as dictionary information for simplicity. Reference numeral 18 in the figure is a number indicating the meaning division.

Next, the example sentence word cutout module 14 performs morphological analysis / syntactic analysis of the example sentence of the dictionary information,
Nouns for thesaurus matching other than headwords are cut out by the same processing as the input character string analysis module. For example, for the example sentence “I am to meet her in the office”, the noun “her” that is the object is cut out because the entry word “meet” is a transitive verb. However, since "her" is in the third person, it is cut out by "she" in the first person. If the cut out noun has a plural form, it is similarly converted into a singular form.

The similarity calculation module 15 compares the cut-out words with each other using the thesaurus stored in the temporary storage means to calculate the similarity. The similarity is basically calculated by the distance on the thesaurus. In other words, the similarity on the thesaurus is the reciprocal of 1 which is the distance if the search keyword and the extracted word are a perfect match on the thesaurus (when matching is successful at the node one level above the end of the thesaurus). Is the similarity. In the case of a partial match (when matching is successful in two or more nodes from the end of the thesaurus), the distance from the terminal of the thesaurus to the intermediate node where matching is successful (the distance between two adjacent upper and lower nodes is 1). The reciprocal of) is the similarity. For example, in the thesaurus of FIG. 3, since "you" and "friend" match at the fourth intermediate node "personal" from the end node, the similarity is 1/4 = 0.25.

FIG. 5 is a diagram showing a similarity calculation processing flow of the similarity calculation module in FIG. Hereinafter, each step will be described in order. step1 : By the matching word combination process, a combination of the words cut out from the inquiry sentence and the example sentence is created for each example sentence. The word "request" extracted from the query
And the word “she” cut out from the example sentence “I am to meet her in the office” is created. A combination is created for every example sentence. If the example sentence is "I am to
In the case of “meet her and him in the office”, the words that are cut out are “she” and “he”, so the combination is
Two are made: "request" and "she", "request" and "he".

[0027] step2: Then, by combining the similarity calculation processing, the similarity of the combination created is calculated. “Reques
The similarity between “t” and “she” and “request” and “he” is 1/5 = 0.2, respectively. Step 3 : Next, the similarity of each example sentence is calculated by the example sentence similarity calculation process. This calculation will take the average of the similarities of the combinations created for each example sentence.
In the case of “to meet her in the office”, the created combination is only “request” and “she”, so 0.2
become. Example sentence “I am to meet her and him in the offic
In case of "e", the created combination is "request" and "sh"
Since there are two "e" and "request" and "he", and the degree of similarity of each is 0.2, the average degree of similarity for each example sentence is 0.2.

[0028] step4: Next, the semantic similarity calculation processing, similarities of each semantic is calculated. This calculation is the average of the similarities of the example sentences included in the word sense. Meaning 1 of FIG.
Contains the example sentence “I am to meet her in the office” and the example sentence “He met a friend”, the similarity of each example sentence is 0.2, so the similarity of each word sense is 0.2. Become.

FIG. 6 is a diagram showing the words cut out from the example sentences and the degrees of similarity for each example sentence and for each word meaning. In FIG.
3 is an example sentence, 24 is a cut-out word, 25 is a similarity degree by example sentence, 26 is a similarity degree by word sense, and 27 is a boundary line indicating a division of word sense. The greater the value of the similarity, the more similar.

The rearrangement module 16 rearranges the dictionary information in order to display the dictionary information. The rearranged result is stored in the temporary storage means. In this case, the word meaning in FIG. 4 is calculated from the similarity calculation result for each word meaning in FIG.
They are rearranged in the order of 4, 3, 1, 2. Also, when the example sentences are sorted in descending order of similarity, they are simply sorted in descending order based on the similarity of each example sentence.
and of the age ”,“ I met the criticism with a smil
e ”,“ I cannot meet your wishes ”,“ I am to meet he
r in the office ”,“ He met a friend ”,“ I'll meet
you at the busstop ”,“ Our company's taxi meets yo
u at the hotel ”, then“ She met the misfortune ”.

The display module 17 displays. FIG. 7 shows a display image when the dictionary information is displayed in descending order of similarity, and FIG. 8 is a display image when the example sentences are rearranged in descending order of similarity.

[0032]

As is apparent from the above description, the present invention has the following effects. (1) By using a thesaurus for search keywords input in natural language, the contents can be displayed in order from the contents that are semantically close to the search keywords. This allows
The user rarely sees unnecessary dictionary information, and it becomes easy to search for a translated word. (2) Further, the expansion can be easily performed so that the user can specify the display content, and only the information of the dictionary required by the user can be efficiently displayed. That is, for example,
By providing the following display modes, the user can obtain the desired information faster.・ Display translated words in order of highest similarity ・ Display only translated words and example sentences in order of highest similarity ・ Display only translated words with highest similarity ・ Display only example sentences with highest similarity ・ Highest similarity Display only the word meaning ・ Display only the translated words and example sentences with the highest degree of similarity (3) Also, to enter search keywords in natural language, use OCR (Optical Character Reader) It is easy to read a language and use it as a search keyword when looking up a dictionary. Further, it is possible to easily specify a range representing a natural language phrase / phrase or the like to be translated for the digitized text, import the buffer, and use it as a search keyword when looking up the dictionary.

[Brief description of drawings]

FIG. 1 is a configuration diagram for explaining an embodiment of an electronic dictionary display device according to the present invention.

FIG. 2 is a diagram showing functional modules for explaining the operation of the electronic dictionary display device according to the present invention.

FIG. 3 is an example of a thesaurus according to the present invention.

FIG. 4 is an example of dictionary information according to the present invention.

5 is a diagram showing a similarity calculation processing flow of a similarity calculation module in FIG.

FIG. 6 is a diagram showing a word example cut out from an example sentence according to the present invention, a similarity example for each example sentence, and a similarity example for each word meaning.

FIG. 7 is a diagram showing a display example of dictionary information sorted in descending order of similarity according to the present invention.

FIG. 8 is a diagram showing a display example of example sentences rearranged in descending order of similarity according to the present invention.

[Explanation of symbols]

1 ... Input means, 2 ... Storage means, 3 ... Temporary storage means, 4 ...
Input character string analysis means, 5 ... data exchange means, 6 ... example sentence word extraction means, 7 ... similarity calculation means, 8 ... rearrangement means,
9 ... Display means, 10 ... Control means.

Claims (2)

[Claims] 1. An input unit for inputting an inquiry sentence including a marked headword, a storage unit for storing a thesaurus, and a temporary storage unit for temporarily storing an intermediate result of processing data or an intermediate result of control. An input character string analysis means for extracting a word for thesaurus matching from a query sentence,
A data exchange means for sending the headword in the inquiry sentence to an external dictionary search device and receiving dictionary information corresponding to the searched headword, an example sentence word extracting means for cutting out a word for thesaurus matching from the example sentence of the dictionary, and a thesaurus. A similarity calculation means for calculating the similarity between the words for thesaurus matching cut out by using the sorting means, a sorting means for sorting the dictionary information in descending order of similarity, a display means for displaying the sorted dictionary information, and An electronic dictionary display device comprising control means for controlling each means, and by using a thesaurus, the dictionary information is rearranged and displayed so that a word meaning semantically close to the inquiry sentence is displayed first. . 2. The electronic dictionary display device according to claim 1, wherein the example sentences in the dictionary information are rearranged and displayed in descending order of similarity.