U.S. patent application number 12/767969 was filed with the patent office on 2010-08-19 for system and method of creating and using compact linguistic data.
This patent application is currently assigned to Research In Motion Limited. Invention is credited to Michael G. ELIZAROV, Vadim FUX.
Application Number | 20100211381 12/767969 |
Document ID | / |
Family ID | 30770900 |
Filed Date | 2010-08-19 |
United States Patent
Application |
20100211381 |
Kind Code |
A1 |
FUX; Vadim ; et al. |
August 19, 2010 |
System and Method of Creating and Using Compact Linguistic Data
Abstract
A system and method of creating and using compact linguistic
data are provided. Frequencies of words appearing in a corpus are
calculated. Each unique character in the words is mapped to a
character index, and characters in the words are replaced with the
character indexes. Sequences of characters are mapped to
substitution indexes, and the sequences of characters in the words
are replaced with the substitution indexes. The words are grouped
by common prefixes, and each prefix is mapped to location
information for the group of words which start with the prefix.
Inventors: |
FUX; Vadim; (Waterloo,
CA) ; ELIZAROV; Michael G.; (Waterloo, CA) |
Correspondence
Address: |
RIM/FINNEGAN
901 New York Avenue NW
Washington
DC
20001
US
|
Assignee: |
Research In Motion Limited
|
Family ID: |
30770900 |
Appl. No.: |
12/767969 |
Filed: |
April 27, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11778982 |
Jul 17, 2007 |
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12767969 |
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10289656 |
Nov 7, 2002 |
7269548 |
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11778982 |
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60393903 |
Jul 3, 2002 |
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Current U.S.
Class: |
704/10 |
Current CPC
Class: |
G06F 40/216 20200101;
Y10S 707/99937 20130101 |
Class at
Publication: |
704/10 |
International
Class: |
G06F 17/21 20060101
G06F017/21 |
Claims
1. A system of creating compact linguistic data, comprising: a
corpus; and a linguistic data analyzer, wherein the linguistic data
analyzer calculates frequencies of words appearing in the corpus,
maps each unique character in the words to a character index,
replaces each character in the words with the character index to
which the character is mapped, maps sequences of characters that
appear in the words to substitution indexes, replaces each sequence
of characters in each word with the substitution index to which the
sequence of characters are mapped, arranges the words into groups
where each group contains words that start with a common prefix,
and maps each prefix to location information for the group of words
which start with the prefix, and wherein the compact linguistic
data includes the unique characters, the character indexes, the
substitution indexes, the location information, the groups of
words, and the frequencies of the words.
2.-13. (canceled)
14. A system of creating compact linguistic data, comprising: a
corpus; and a linguistic data analyzer, wherein the linguistic data
analyzer calculates frequencies of words appearing as independent
words in the corpus, maps each unique character in the words to a
character index, replaces each character in the words with the
character index to which the character is mapped, maps sequences of
characters that appear in the words to substitution indexes,
replaces each sequence of characters in each word with the
substitution index to which the sequence of characters are mapped,
arranges the words into groups where each group contains words that
start with a common prefix, maps each prefix to location
information for the group of words which start with the prefix to
create a prefix index, and removes the prefix from the words in the
groups of words; and wherein the compact linguistic data includes
the unique characters, the character indexes, the substitution
indexes, the prefix index, the groups of words, and the frequencies
of the words.
15. The system of claim 14, further comprising: a user interface,
comprising: a text input device; and a text output device; and a
text input logic unit, wherein the text input logic unit receives a
text prefix from the text input device, retrieves a plurality of
predicted words from the compact linguistic data that start with
the text prefix, selects one of the plurality of predicted words
for display based on the frequencies of the plurality of predicted
words, and displays the one predicted words using the text output
device.
16. The system of claim 15, wherein the text input device is a
keyboard.
17. The system of claim 16, wherein the keyboard is a reduced
keyboard.
18. The system of claim 15, wherein the user interface and the text
input logic unit are implemented on a mobile communication
device.
19. The system of claim 15, wherein the text input logic unit
selects one of the groups of words as the plurality of predicted
words.
20. The system of claim 19, wherein the selected group of words is
selected based on the frequency of the group of words.
21. The system of claim 15, wherein the text input logic unit is
configured to update the frequencies of the words based on whether
the predicted word is input by a device user.
22. The system of claim 14, wherein for each group, only the
maximum frequency, which is the highest frequency value in the
group, is retained with full precision, and the frequencies of
words with less than the maximum frequency are retained as a
percentage of the maximum frequency.
23. A computer-implemented method of creating compact linguistic
data, comprising: performing, by a processor, the operations of:
calculating frequencies of words appearing as independent words in
the corpus, mapping each unique character in the words to a
character index, replacing each character in the words with the
character index to which the character is mapped, mapping sequences
of characters that appear in the words to substitution indexes,
replacing each sequence of characters in each word with the
substitution index to which the sequence of characters are mapped,
arranging the words into groups where each group contains words
that start with a common prefix, mapping each prefix to location
information for the group of words which start with the prefix to
create a prefix index, and removing the prefix from the words in
the groups of words; and storing, in electronic format, the unique
characters, the character indexes, the substitution indexes, the
prefix index, the groups of words, the frequencies of the words and
the frequencies of the groups of words as compact linguistic
data.
24. The method of claim 23, further comprising: receiving a text
prefix from a text input logic unit of a text input device,
retrieving a plurality of predicted words from the compact
linguistic data that start with the text prefix, selecting one of
the plurality of predicted words for display based on the
frequencies of the plurality of predicted words, and displaying the
one predicted words using a text output device.
25. The method of claim 24, wherein the text input device is a
keyboard.
26. The method of claim 25, wherein the keyboard is a reduced
keyboard.
27. The method of claim 24, wherein the text input logic unit is
implemented on a mobile communication device.
28. The method of claim 24, wherein the text input logic unit
selects one of the groups of words as the plurality of predicted
words.
29. The method of claim 28, wherein the selected group of words is
selected based on the frequency of the group of words.
30. The method of claim 24, wherein the text input logic unit is
configured to update the frequencies of the words based on whether
the predicted word is input by a device user.
31. The method of claim 23, wherein for each group, only the
maximum frequency, which is the highest frequency value in the
group, is retained with full precision, and the frequencies of
words with less than the maximum frequency are retained as a
percentage of the maximum frequency.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This is a continuation of U.S. patent application Ser. No.
10/289,656, filed on Nov. 7, 2002, which claims priority from U.S.
Provisional Application Ser. No. 60/393,903, filed on Jul. 3,
2002.
BACKGROUND
[0002] 1. Field of the Invention
[0003] The present invention relates in general to linguistic data,
and in particular to storage and use of the linguistic data for
text processing and text input.
[0004] 2. Description of the State of the Art
[0005] The growing use of mobile devices and different types of
embedded systems challenges the developers and manufacturers of
these devices to create products that require minimal memory usage,
yet perform well. A key element of these products is the user
interface, which typically enables a user to enter text which is
processed by the product.
[0006] One application of linguistic data is to facilitate text
entry by predicting word completions based on the first characters
of a word that are entered by a user. Given a set of predictions
that are retrieved from the linguistic data, the user may select
one of the predictions, and thus not have to enter the remaining
characters in the word.
[0007] The prediction of user input is especially useful when
included in a mobile device, since such devices typically have
input devices, including keyboards, that are constrained in size.
Input prediction minimizes the number of keystrokes required to
enter words on such devices.
[0008] Input prediction is also useful when text is entered using a
reduced keyboard. A reduced keyboard has fewer keys than characters
that can be entered, thus keystroke combinations are ambiguous. A
system that uses linguistic data for input prediction allows the
user to easily resolve such ambiguities. Linguistic data can also
be used to disambiguate individual keystrokes that are entered
using a reduced keyboard.
[0009] Existing solutions for storage of linguistic data used for
text input and processing typically rely on hash tables, trees,
linguistic databases or plain word lists. The number of words
covered by these linguistic data formats is limited to the words
which have been stored.
[0010] The linguistic data which is used in existing text input
prediction systems is typically derived from a body of language,
either text or speech, known as a corpus. A corpus has uses such as
analysis of language to establish its characteristics, analysis of
human behavior in terms of use of language in certain situations,
training a system to adapt its behavior to particular linguistic
circumstances, verifying empirically a theory concerning language,
or providing a test set for a language engineering technique or
application to establish how well it works in practice. There are
national corpora of hundreds of millions of words and there are
also corpora which are constructed for particular purposes. An
example of a purpose-specific corpus is one comprised of recordings
of car drivers speaking to a simulation of a voice-operated control
system that recognizes spoken commands. An example of a national
corpus is the English language.
SUMMARY
[0011] A system of creating compact linguistic data is provided.
The system comprises a corpus and linguistic data analyzer. The
linguistic data analyzer calculates frequencies of words appearing
in the corpus. The linguistic data analyzer also maps each unique
character in the words to a character index, and replaces each
character in the words with the character index to which the
character is mapped. The linguistic data analyzer also maps
sequences of characters that appear, in the words to substitution
indexes, and replaces each sequence of characters in each word with
the substitution index to which the sequence of characters are
mapped. The linguistic data analyzer also arranges the words into
groups where each group contains words that start with a common
prefix, and maps each prefix to location information for the group
of words which start with the prefix. The compact linguistic data
includes the unique characters, the character indexes, the
substitution indexes, the location information, the groups of words
and the frequencies of the words.
[0012] A compact linguistic data structure for a plurality of words
is also provided. The words are organized into groups, each group
containing words that have a common prefix. The compact linguistic
data structure comprises an alphabet comprised of each unique
character in the words, a character-mapping table for mapping each
character in the alphabet to a character index, a substitution
table for mapping sequences of characters from the words to
substitution indexes, and a plurality of word definition tables for
storing the words. Each word definition table stores each of the
words included in one of the groups. The compact linguistic data
structure further comprises an offset table for locating the word
definition tables. For each of the common prefixes, the offset
table contains a location of the word definition table which stores
words starting with the common prefix. Each of the words in the
word definition tables is encoded by replacing each character in
the word with the character index to which the character is mapped
by the character-mapping table, and by replacing each sequence of
characters from the substitution table that appears in the word
with the substitution index to which the sequence of characters is
mapped by the substitution table. The common prefixes for words in
each word definition table are removed.
[0013] A method of creating compact linguistic data is also
provided. The method begin with a step of creating a word-list
comprising a plurality of words occurring most frequently in a
corpus. The method continues with a step of sorting the words in
the word-list alphabetically. The method continues with a step of
creating a character-mapping table for encoding the words in the
word-list by replacing characters in the words with associated
character indexes contained in the character-mapping table. The
method continues with a step of separating the words in the
word-list into groups, wherein words in each group have a common
prefix. The method continues with a step of creating a substitution
table for encoding the words in the groups by replacing character
sequences in the words in the groups with substitution indexes that
are mapped to the character sequences by the substitution table.
The method continues with a step of encoding the words in the
groups into byte sequences using the character-mapping table and
the substitution table. The method continues with a step of
creating word definition tables and storing the encoded words in
the word definition tables. The method continues with a step of
creating an offset table for locating groups of encoded words. The
method ends with a step of storing the character-mapping table, the
substitution table, the word definition tables, and the offset
table.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a block diagram of a system in which linguistic
data is used for text input prediction;
[0015] FIG. 2 is a block diagram of a system of creating compact
linguistic data;
[0016] FIG. 3 is flowchart illustrating a method of filtering
source files;
[0017] FIG. 4 is flowchart illustrating a method of word frequency
calculation;
[0018] FIG. 5 is a flowchart illustrating a method creating compact
linguistic data;
[0019] FIG. 6 is a block diagram of a format of compact linguistic
data;
[0020] FIG. 7 is a block diagram of a complex word definition
table;
[0021] FIG. 8 is a flowchart illustrating a method of frequency
modification; and
[0022] FIG. 9 is a flowchart illustrating a method of inflection
analysis.
DETAILED DESCRIPTION
[0023] A system and method of creating and using compact linguistic
data, based on word prefix indexing with statistical character
substitution, is provided. The method by which the system stores
the linguistic data requires minimal memory usage and provides very
fast access to words which begin with a specified prefix and their
associated frequencies.
[0024] FIG. 1 is a block diagram of a system in which linguistic
data is used for text input prediction. The system includes
linguistic data 100, a text input logic unit 102, and a user
interface 103. The system can be implemented on any computing
device requiring text input, but is especially suited for embedded
devices with a slow CPU and significant RAM and ROM limitations,
such as a mobile communication device.
[0025] The user interface 103 includes a text input device 104,
which allows a user to enter text into the system. The text input
device 104 is any device that enables text entry, such a QWERTY,
AZERTY or Dvorak keyboard, or a reduced keyboard. The user
interface 103 also includes a text output device 106, which
displays text to a user. The text output device 106 may be a
graphical component presented on the screen of a mobile device or
computer.
[0026] The linguistic data 100 is based on word prefix indexing
with statistical character substitution, and is described in more
detail below.
[0027] The text input logic unit 102 may, for example, be
implemented by computer instructions which are executed by a
computer processor that is contained in a mobile device.
[0028] The text input logic unit 102 receives text that is entered
by a user using the text input device 104. The text input logic
unit 102 then uses the text output device 106 to present the user
with predictions of words that the user has started to enter. The
predictions are the most probable complete words that start with
prefixes entered as text by the user, and are retrieved by the text
input logic unit 102 from the linguistic data 100. The user may
then select one of the predictions using the text input device
104.
[0029] Where the text input device 104 is a reduced keyboard, the
text input logic unit 102 also disambiguates individual keystrokes
that are received from the reduced keyboard, presenting the user
with the most probable characters based on words in the linguistic
data 100.
[0030] FIG. 2 is a block diagram of a system of creating compact
linguistic data. The linguistic data analyzer 202 creates
linguistic data 204, described in detail below, by analyzing the
corpus 200 of a natural language, such as English or French. The
linguistic data analyzer 202 calculates frequencies of words
appearing in the corpus 200, maps each unique character in the
words to a character index, replaces characters in the words with
the character indexes to which the characters are mapped, maps
sequences of characters that appear in the words to substitution
indexes, replaces the sequences of characters in the words with the
substitution indexes to which the sequences of characters are
mapped, arranges the words into groups where each group contains
words that start with a common prefix, and maps each prefix to
location information for the group of words which start with the
prefix.
[0031] The analysis of the corpus 200 by the linguistic data
analyzer 202 includes the calculation of the absolute frequency of
the unique words appearing in the corpus 200. Methods for the
calculation of frequency and creation of a word-list are described
in FIG. 3 and FIG. 4. Once a word-list has been derived from the
corpus 200, the word-list is used to create the linguistic data
204. The linguistic data 204 includes the unique characters, the
character indexes, the substitution indexes, the location
information, the groups of words and the frequencies of the words.
A method for creating the linguistic data 204 is described in FIG.
5. The linguistic data 204 produced by the linguistic data analyzer
202 illustrated in FIG. 6.
[0032] The absolute frequency of a certain group of words found in
the corpus 200 may alternatively be modified by separating this
group to a different file and assigning a custom weight to this
file. This group may consist of words which are domain specific,
such as names of places or medical terms, and which, based on user
preferences, must be included in the resulting word-list. As a
result, the absolute value of the frequencies for this group of
words will be modified using the weight assigned to the group, so
that this group of words will have frequencies that are different
they would have otherwise had.
[0033] FIG. 3 is flowchart illustrating a method of filtering
source files. The source files contain text which comprises a
corpus. The filtering method is the first step in calculating the
frequency of words in the corpus.
[0034] The method begins with the step 300 of reading the contents
a source file. After the source file is read, the method continues
with the step 302 of performing substitution of text from the file
according to user preferences, which may be stored in a properties
file. The user preferences specify regular expressions which are
applied to the text in order to substitute invalid or unwanted
characters. For example, a user may not want street names included
in the word list, or an Italian user may want to replace "e"
followed by a non-letter with "e", or a user may want to skip the
last sentence of a text when it is expected that the last sentence
contains only the author's name.
[0035] The method then continues with the step 304 of obtaining a
filter corresponding to the type indicated by the file extension of
the source file. For example, if the file extension is ".xml", it
is assumed that the file contains an eXtensible Markup Language
(XML) document, so an XML filter is obtained. Similarly, if the
file extension is ".html", then a HyperText Markup Language (HTML)
filter is obtained, and if the file extension is ".txt", then a
text filter is obtained. Other file extensions may also be mapped
to additional filters.
[0036] The filter obtained at step 304 is then applied at step 306
in order to remove words which are not part of the corpus, but
rather are part of format definitions. For example, an XML filter
removes mark-up tags from the text read from the file.
[0037] The method continues with the step 308 of extracting the
words from the data resulting from step 306, and writing the
extracted words to a filtered-words file at step 310.
[0038] If it is determined at step 312 that there are more source
files to filter, then the method continues at step 300. Otherwise,
the method ends at step 314. When the method ends, all of the
source files which comprise the corpus have been filtered.
[0039] FIG. 4 is flowchart illustrating a method of word frequency
calculation. The method utilizes the filtered-words files that were
produced by the method illustrated in FIG. 3. The words from the
filtered-words file are loaded into a word-tree. The word-tree is
an effective structure to store unique words and their frequencies
using minimal memory. The tree is organized such that words that
occur frequently in the filtered-words files are located in the
inner nodes of the tree, and words that occur less frequently are
located in the leaves of the tree. Each node of the tree contains a
unique word and the word's absolute frequency. Words are added and
deleted from the tree in a fashion that assures that the tree
remains balanced.
[0040] The method begins with the step 400 of reading a
filtered-words file. The method continues with the step 402 of
reading a word from the filter-words file and adding it into the
word-tree, if the word is not already in the word-tree. The
frequency associated with the word in the tree is incremented.
[0041] The method continues at step 404, where it is determined if
the number of nodes in the tree exceeds a predefined limit, which
may be specified in a properties file. If the size of the word-tree
does not exceed the limit, then the method continues at step 408.
Otherwise, the method continues at step 406.
[0042] At step 406, the word-tree is shrunk so that it no longer
exceeds the size limit. The tree is shrunk by deleting the
least-frequently used words from the tree, which are located in the
leaf nodes. The method then continues at step 408.
[0043] Step 408 determines whether there are any filtered words
left in the filtered-words file. If there are, then the method
continues at step 402. If there are no filtered words left, then
the method continues at step 410.
[0044] Step 410 determines whether there are any remaining
filtered-words files to process. If there are, then the method
continues at step 400. Otherwise, the method continues at step
412.
[0045] At step 412, a word-list which stores words which have been
added to the word-tree and their frequencies are written to an
output file.
[0046] The method illustrated in FIG. 4 allows even very large
corpora to be processed by a single computer. The resulting
word-list contains up to a predefined limited number of most
frequently occurring words in the corpus, and the absolute
frequencies associated with the words.
[0047] FIG. 5 is a flowchart illustrating a method creating compact
linguistic data. The method uses a word-list containing word
frequency information to produce compact linguistic data, and
includes word prefix indexing and statistical character
substitution.
[0048] The method beings at step 500, where the word-list is read
from an output file that was produced by a method of word frequency
calculation such as the method illustrated in FIG. 4. The words in
the word-list are then sorted alphabetically.
[0049] The method continues with step 501 of normalizing the
absolute frequencies in the word-list. Each absolute frequency is
replaced by a relative frequency. Absolute frequencies are mapped
to relative frequencies by applying a function, which may be
specified by a user. Possible functions include a parabolic,
Gaussian, hyperbolic or linear distribution.
[0050] The method continues with the step 502 of creating a
character-mapping table. The character-mapping table is used to
encode words in a subsequent step of the method. When encoding is
performed, the characters in the original words are replaced with
the character indexes of those characters in the character-mapping
table. Since the size of the alphabet for alphabetical languages is
much less than 256, a single byte is enough to store Unicode
character data. For example, the Unicode character 0x3600 can be
represented as 10 if it is located at index 10 in the
character-mapping table. The location of a character in the
character-mapping table is not significant, and is based on the
order that characters appear in the given word-list.
[0051] The method continues with the step 504 of separating the
words in the word-list into groups. Words in each group have a
common prefix of a given length and are sorted by frequency. Words
are initially grouped by prefixes that are two characters long. If
there are more than 256 words that start with the same
two-character prefix, then additional separation will be performed
with longer prefixes. For example, if the word-list contains 520
words with the prefix "co", then this group will be separated into
groups with prefixes "corn", "con", and so on.
[0052] The method continues with the step 506 of producing a
frequency set for each group of words. In order to reduce the
amount of space required to store frequency information, only the
maximum frequency of words in each group is retained with full
precision. The frequency of each other word is retained as a
percentage of the maximum frequency of words in its group. This
technique causes some loss of accuracy, but this is acceptable for
the purpose of text input prediction, and results in a smaller
storage requirement for frequency information.
[0053] The method continues with step 508. In order to reduce the
amount of data required to store the words in the word-list, the
character sequences that occur most frequently in the words are
replaced with substitution indexes. The substitution of n-grams,
which are sequences of n-number of characters, enables a number of
characters to be represented by a single character. This
information is stored in a substitution table. The substitution
table is indexed, so that each n-gram is mapped to a substitution
index. The words can then be compacted by replacing each n-gram
with its substitution index in the substitution table each time the
n-gram appears in a word.
[0054] The method continues with step 510 of encoding the word
groups into byte sequences using the character-mapping table and
the substitution table, as described above. The prefixes used to
collect words into groups are removed from the words themselves. As
a result, each word is represented by a byte sequence, which
includes all the data required to find the original word, given its
prefix.
[0055] The method continues with step 511 of creating word
definition tables. The word definition tables store the frequency
sets calculated at step 506 and the encoded words produced at
510.
[0056] The method continues with step 512 of creating an offset
table. The offset table contains byte sequences that represent the
groups of words. This table enables the identification of the start
of a byte sequences that represents a particular word group. The
offset table is used to locate the byte sequences that comprise the
encoded words for a particular group that start with a common
prefix.
[0057] The method concludes with step 514. At this step, the
linguistic data resulting from the method has been stored in the
tables that have been created. The data tables, including the
character-mapping table, the substitution table, the offset table
and the word definition tables, are stored in an output file.
[0058] Statistical data gathered during the method of creating
compact linguistic data may optionally be stored at step 514. The
statistical data includes the frequency with which n-grams stored
in the substitution table appear in words in the linguistic data,
the number of words in the linguistic data, word-list and corpus
from which the word-list was generated, and ratios between the
numbers of words in the linguistic data, word-list and corpus.
[0059] FIG. 6 is a block diagram of a format of compact linguistic
data. The primary objective of the data format is to preserve the
simplicity of interpretation of the linguistic data, while
minimizing memory use and the number of computer instructions
required to create and interpret the data. Linguistic data in the
format is produced by the linguistic data analyzer 202 (FIG. 2),
and is the output of the method illustrated by FIG. 5.
[0060] The format allows linguistic data to be stored with or
without word frequency information. When the linguistic data
includes frequency information, learning capabilities, which are
described below, can be implemented, and the data can be used to
predict input entered with a reduced keyboard. If frequency
information is not included, then words which are less than three
characters long are not included, since they will not be useful for
predicting user input.
[0061] The format defines the structure of a computer file which
contains a header 602 followed by a number of tables.
[0062] The header 602 contains a signature including a magic
number, which is a number identifying the format of the file. The
header 602 also contains information which specifies the version
and priority of the linguistic data contained in the file. Priority
information is used to assign relative importance to the linguistic
data when multiple files containing linguistic data are used by a
text input logic unit. The header 602 also indicates whether the
file includes frequency information.
[0063] The header 602 is followed by the index table 604. The index
table 604 contains indexes in the file to the remaining tables
which are defined below, and also allows for additional tables to
be added. A table is located using the index information found at
the table's entry in the index table 604.
[0064] The index table 604 is followed by the name table 606. The
name table 606 contains a name which identifies the word-list.
[0065] The name table 606 is followed by the character-mapping
table 608. The character-mapping 608 table contains the alphabet
being used for this word-list, and maps each character in the
alphabet to a character index. The alphabet consists of each unique
character used in words in the word-list.
[0066] The character-mapping table 608 is followed by the
substitution table 610. The substitution table 610 contains a
bi-gram substitution table, followed by a table for each group of
higher-order n-grams which are defined, such as tri-grams,
four-grams, and so on. Each n-gram is mapped to a substitution
index by the substitution table 610.
[0067] The substitution table 610 is followed by the offset table
612. This table is used to locate a word definition table,
described below, based on the common prefix of words in the word
definition table to be located. For each combination of two
characters in the alphabet, the table contains the offset in the
file of a word definition table that contains words that start with
that combination of characters. For empty groups, the offset is
equal to the next non-empty offset. Each offset also specifies
whether the word definition table located at the offset in the file
is simple or complex, as described below.
[0068] Given a two-character sequence, the offset is located at the
index in the offset table defined by the formula: ((position of the
first character in the alphabet*number of characters in the
alphabet)+position of the second character in the alphabet). For
example, if the alphabet is English, then the size of the alphabet
is 26, so the index of "ab" in the offset table is ((0*26)+1),
which equals 1. Hence, the size of the offset table 612 is based on
the length of the alphabet.
[0069] An inflection table, not shown, may optionally be included
in the linguistic data. The inflection table stores word suffixes
which may be used in word definitions. A method of inflection
analysis in illustrated in FIG. 9.
[0070] The linguistic data also contains word definition tables
614. A word definition table stores words from a single word group
and frequencies associated with the words, and can be either simple
or complex. A simple table is used to define words which are
grouped by two-character prefixes only. A complex table is used to
define words which are grouped by prefixes of greater lengths.
[0071] Words in the definition tables 614 are encoded using the
character-mapping table 608 and the substitution table 610. The
characters in the words are replaced with the corresponding
character indexes from the character-mapping table 608, and the
n-grams that are in the substitution table 610 are replaced in the
words with their corresponding substitution indexes in the
substitution table 610. Since the offset table 612 uniquely maps
each bi-gram prefix in the alphabet to a location in the file that
defines words that start with that prefix, the prefixes do not need
to be retained, and thus are removed from the word definitions.
[0072] Upper case words may optionally be marked with an additional
special character. The special character is stored in the
character-mapping table 608, extending the alphabet with an
additional character not used in the language of the words in the
word-list.
[0073] A simple word definition table contains the encoded words of
a group, and the frequencies associated with the words. The
frequencies are normalized by applying a normalization function
which converts the frequencies so that their values are within a
predetermined range. Only the maximum frequency of words in the
group is stored with full precision in the table. All other
frequencies are stored as percentages of the maximum frequency. The
encoded words are sorted by frequency. However, if learning
capabilities are applied, as described below, then the initial
sorting is no longer valid, and the encoded words may need to be
resorted.
[0074] As will be appreciated by those skilled in the art,
characters are represented in computer systems by sequences of
bits. The words in the word definition tables 614 are separated by
characters with the most significant bit set. If a character has
its most significant bit set, then it is the last character in a
word. The character is then treated as if its most significant bit
were not set for the purpose of determining the value of the
character, so that the most significant bit does not affect the
value of the character.
[0075] FIG. 7 is a block diagram of a complex word definition
table. The complex word definition table is recursive, in that it
contains local word definition tables 708, each of which is a
simple or complex word definition table as described above.
[0076] The local word definition tables 708 define words that are
grouped by higher order n-gram prefixes. Each of the local word
definition tables 708 stores words stored by the word definition
table that have a common prefix, where the common prefix for words
in each of the local word definition tables 708 is longer than the
common prefix for words in the word definition table. The common
prefixes of words in the local word definition tables 708 are
removed.
[0077] For example, if a word group includes words which start with
the prefix "co", and there more than 256 words that start with that
prefix, then the complex word definition table for "co"-prefixed
words contains local word definition tables 708 that define words
that start with "corn", "con", and so on. The table for
"com"-prefixed words could be a complex word definition table that
further contains local word definition tables 708 for words
starting with "comm" and "comp", while the table for "con"-prefixed
words could be a simple word definition table that contains only
words starting with "con".
[0078] In addition to containing local word definition tables 708,
each word definition table includes a local offset table 706, which
is used to locate each of the local word definition tables 708.
Each offset also indicates whether the table that is referred to by
the offset is a complex or simple word definition table.
[0079] Each complex word definition table also includes a local
character-mapping table 704. This table is functionally the same as
the character-mapping table 608 (FIG. 6), except that it only
contains characters are included in words that are in local word
definition tables 708. The local character-mapping table 704 maps
each character in the words in the local word definition tables 708
to a local character index. Words in simple local word definition
tables are encoded by replacing characters in the words with the
local character indexes.
[0080] A complex word definition table also contains a hotword
table 700 and an exception table 702. Hotwords are the words
associated with the highest frequencies in the group contained in
the complex word definition table. The hotword table 700 contains
indexes of hotwords that are located in local word definition
tables 708 that are simple word definition tables. The exception
table 702 stores hotwords that are located in local word definition
tables 708 that are complex word definition tables. A hotword can
be retrieved quickly using the hotword table 700 and the exception
table 702, instead of performing a search of the local word
definition tables 708 to find the hotword.
[0081] The format of linguistic data described above enables
determination of word predictions very quickly, using a minimal
amount of memory. When a user enters a word prefix using a text
input device that maps characters to unique keys or key
combinations, such as a QWERTY keyboard, a text input logic unit
retrieves the words in the linguistic data that start with the
prefix having the highest frequencies, and presents the predictions
to the user. When the user starts to type a word using a reduced
keyboard, the word prefix is ambiguous, since each key on a reduced
keyboard is mapped to multiple characters. In this case, a text
input logic unit retrieves predictions from the linguistic data
that start with any of the combinations of characters that
correspond to the prefix entered by the user.
[0082] The format also allows for easy modification of the words'
frequencies, to conform to individual user's text input habits. The
user's habits, confirmed by the input choices he or she makes when
presented with word prediction alternatives, are learned by the
text input logic unit and stored in tables including those
described below.
[0083] Learning capabilities include the modification of frequency
information for words, and the addition of words to the linguistic
data. Both operations are based on similar processes of adding the
words and corresponding frequency information into a learning
word-list. The learning word-list includes tables for frequency
modification and for the addition of new words.
[0084] FIG. 8 is a flowchart illustrating a method of frequency
modification. The method proceeds on the assumption that the base
linguistic data, which is the linguistic data compiled as described
above before any learning data is gathered, has correct frequency
information in general. Therefore, the method allows for limited
modification of the frequency information.
[0085] The method starts with the step 802 of adding a
user-selected word 800 to the learning word-list. The user-selected
word 800 is the word selected by the user from the list of
predicted words offered that begin with a word prefix entered by
the user. The user selects a prediction using a text input device.
The selected word is added to the learning word-list.
[0086] The method continues with step 804 of obtaining the word
with the maximum frequency of words in the prediction list that was
presented to the user. The words in the prediction list and their
corresponding frequencies may have been obtained from the word
definition tables in base linguistic data, or from the learning
word-list. If it is determined at step 806 that the word with
maximum frequency was obtained from the word definition tables,
then the method continues at step 808, and the user-selected word
800 is assigned a frequency equal to the maximum frequency plus
one.
[0087] If it is determined at step 806 that the word with maximum
frequency was not obtained from the word definition tables, but was
rather obtained from the learning word-list, then the method
continues at step 810, and the user-selected word 800 is assigned a
frequency that is equal to the maximum frequency. The method then
ends with step 812 of deleting the word with maximum frequency
obtained at step 804 from the learning word-list.
[0088] The following paragraphs are examples of the method
illustrated in FIG. 8. Each example assumes that the user enters a
three-character prefix.
[0089] Given the three-character prefix of "beg", and predictions
"began", which has a frequency of 3024, "begin", which has a
frequency of 2950, "beginning", which has a frequency of 2880, and
"begins", which has a frequency of 2000, where all words are
obtained from the word definition tables in the base linguistic
data, if the user selects the word "begin", then the word "begin"
is added to the learning word-list with the frequency 3025.
[0090] Given the same three-character prefix "beg", and predictions
"begin", which has a frequency of 3025, "began", which has a
frequency of 3024, "beginning", which has a frequency of 2880, and
"begins", which has a frequency of 2000, where "begin" is obtained
from the learning word-list, if the user selects "began", then the
word "began" is added to learning word-list with the frequency
3025, and word "begin" is deleted from learning word-list.
[0091] The following is an example of the method of FIG. 8 where
the three-character prefix is entered using a reduced keyboard. The
reduced keyboard includes a key for entering "a", "b" or "c", a key
for entering "n" or "o", and a key for entering "w", "x", or "y".
In this example, it is assumed that the user enters the
three-character prefix by pressing the "a/b/c" key, then the "n/o"
key, and finally the "w/x/y" key. Given the predictions "any",
which has a frequency of 3024, "boy", which has a frequency of
2950, "box", which has a frequency of 2880, "bow", which has a
frequency of 2000, "cow", which has a frequency of 1890, and "cox",
which has a frequency of 1002, where all of the words are obtained
from word definition tables in the base linguistic data, if user
selects "boy", then the word "boy" is added to learning word-list
with a frequency 3025.
[0092] The learning word-list includes an updated frequencies table
that contains words with updated frequency and a new words table
that contains new words. Both of these tables include words which
are encoded as in the base linguistic data, using the same
character-mapping 608 (FIG. 6) and substitution tables 610 (FIG. 6)
as are used by the base linguistic data. Each learning word-list
table also includes indexes for the beginnings of words in the
table, frequency information associated with the words in the
table, and a sorting index that specifies the alphabetically sorted
order of the words. Each table also includes a flag which indicates
whether the table contains updated frequencies or new words. The
learning word-list tables follow sequentially one after the other,
with the updated frequencies table appearing first.
[0093] If the learning word-list tables reach a maximum-defined
length, then the oldest words from the tables are deleted in order
to make room for new entries in the tables.
[0094] Adding words to and deleting words from a learning word-list
table are performed by creating the byte sequence representing the
updated table and simultaneously writing the byte sequence into an
output stream. After the update is complete, the updated data is
reread. The process of writing into an output stream occurs every
time words are added or deleted from the learning word-list.
[0095] In order to add or delete words from one of the learning
word-list tables, the alphabet in the character-mapping table 608
(FIG. 6) is updated if it doesn't contain the characters that
appear in words to be added. Words to be added are then encoded
using the character-mapping table 608 (FIG. 6) and the substitution
table 610 (FIG. 6), and inserted into the beginning of the new
words table. Finally, the frequencies and sorting index of the
learning word-list table are updated.
[0096] FIG. 9 is a flowchart illustrating a method of inflection
analysis. The system and method of creating compact linguistic data
may alternatively include the method of inflection analysis, in
which both a list of words that have frequencies higher than a
minimum specified frequency and an inflection table are created.
The inflection table is created based on statistical suffix
analysis, and encapsulates the linguistic rules for word creation
in the language of the corpus. The inflection tables make it
possible to produce more than one word using the basic word forms
stored in the inflection table, ensuring that more words are
covered by the linguistic data, while the basic word-list remains
compact. An inflection table may optionally be included in the
linguistic data format shown in FIG. 6.
[0097] The method begins with the step 900 of finding a configured
number of words that occur most frequently in the word-list, based
on the absolute frequency of the words.
[0098] The method continues with the step 901 of finding suffixes
of the frequently occurring words. The step of suffix finding is
based on an iterative search of suffixes of decreasing length,
starting with suffixes that are six characters long and ending with
suffixes that are two characters long. These suffixes do not always
match the existing counterparts in the grammar of the given
language, but rather the suffix finding is based on the number of
occurrences of suffixes in the word-list.
[0099] The method continues with the step 902 of updating the
inflection table with the suffixes found in the previous step. The
first time step 902 is performed, the inflection table is created
before it is updated.
[0100] At step 903, if the size of the linguistic data is smaller
than a configured maximum size, then the method continues at step
901. Otherwise, the method concludes with the step 904 of creating
a list of the words in the word-list without the suffixes contained
in the inflection table.
[0101] The inflection table and the list of words without suffixes
can then be encoded as described above in reference to FIG. 5. When
the method of inflection analysis is used, the resulting compact
linguistic data as illustrated in FIG. 6 also includes the
inflection table. The words in the word definition tables 614 (FIG.
6) then do not include the suffixes that are included in the
inflection table, but rather contain references to the suffixes in
the inflection table. The space saved by using the inflection table
for each suffix stored is the number of occurrences of the suffix,
multiplied by the length of the suffix.
[0102] The above description relates to one example of the present
invention. Many variations will be apparent to those knowledgeable
in the field, and such variations are within the scope of the
application.
[0103] For example, while the language used in most of the examples
is English, the system and method provided creates compact
linguistic data for any alphabetical language.
[0104] In addition, the system and method of creating and using
compact linguistic data can be implemented as software, firmware,
or hardware, or as a combination thereof, on personal computers,
PDAs, cellular telephones, two-way pagers, wearable computers of
any sort, printers, set-top boxes and any other devices allowing
text input and display.
[0105] Also, the methods illustrated in FIGS. 3, 4, 5, 8 and 9 may
contain fewer, more or different steps than those that are shown.
For example, although the methods describe using computer files to
store final and intermediate results of the methods, the results
could also be stored in computer memory such as RAM or Flash memory
modules.
* * * * *