CN112181163A

CN112181163A - Input method, input device and input device

Info

Publication number: CN112181163A
Application number: CN201910605239.4A
Authority: CN
Inventors: 余天照
Original assignee: Beijing Sogou Technology Development Co Ltd
Current assignee: Beijing Sogou Technology Development Co Ltd
Priority date: 2019-07-05
Filing date: 2019-07-05
Publication date: 2021-01-05

Abstract

The embodiment of the invention provides an input method, an input device and a device for inputting. The method specifically comprises the following steps: searching in the multivariate relational data according to the context corresponding to the input string to obtain a hit element corresponding to the context; determining a target hit element corresponding to the input string from the hit elements; and determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string. The embodiment of the invention can provide the long word association function corresponding to the input string under the condition of reducing the consumption of equipment resources, thereby improving the input efficiency.

Description

Input method, input device and input device

Technical Field

The present invention relates to the field of input technologies, and in particular, to an input method, an input device, and an input device.

Background

The device is used as a bridge for the communication between the computer system and the user or other devices, is one of main devices for information interaction between the user and the computer system, and can facilitate the user to input information in various scenes. For example, a user may input a keyword in a search engine to search for a web page, may input a text in an instant messaging APP (Application) to communicate with other users, may input a text in a document APP to edit a document, and so on.

The association function of the input method is an extended function of an input method program, and the occurrence of the association function reduces the times of active input and key pressing of a user and increases the intelligence of the input method. One implementation of the long word association function includes: firstly, searching a corresponding common candidate in a word stock according to an input string of a user; and then, matching the common candidates with left elements in a binary library, and obtaining a first candidate according to the right elements successfully matched. For example, if the input string is "jnttq", the first candidate "today weather good" can be obtained by using the implementation process described above, where "good" is the right element of successful matching obtained from "jnttq".

With the growth of internet corpora, the number of entries in the lexicon is gradually increased, which results in a larger number of common candidates corresponding to the input string, and further results in a larger computation amount corresponding to the matching of the binary library, thereby increasing the resource consumption of the device. Particularly, when the input string is a simple pinyin string and/or the input string is input through a squared figure key, the number of common candidates corresponding to the input string is particularly large. For example, when the squared key "9" is pressed, all entries beginning with wxyz satisfy the conditions for common candidates, which results in a huge amount of computation for matching the binary library. Therefore, in order to reduce the resource consumption of the device, the long word association function is not provided in the case where the input string is short. The word length corresponding to the input string required by the long-word association function is generally 4 at present, that is, the long-word association function is provided only when the input string is matched with 4 words in a word stock, otherwise, if the word length corresponding to the input string is less than 4, the long-word candidate function is not provided, for example, the input string is "w" or "jnt", and the current input method cannot provide corresponding long-word candidates.

Disclosure of Invention

The embodiment of the invention provides an input method, an input device and an input device, which can provide a long word association function corresponding to an input string under the condition of reducing the consumption of equipment resources, and further can improve the input efficiency.

In order to solve the above problem, an embodiment of the present invention discloses an input method, including:

searching in the multivariate relational data according to the context corresponding to the input string to obtain a hit element corresponding to the context;

determining a target hit element corresponding to the input string from the hit elements;

and determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string.

In another aspect, an embodiment of the present invention discloses an input device, including:

the searching module is used for searching in the multivariate relational data according to the context corresponding to the input string to obtain a hit element corresponding to the context;

the target hit element determining module is used for determining a target hit element corresponding to the input string from the hit elements; and

and the first candidate determining module is used for determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string.

In yet another aspect, an embodiment of the present invention discloses an apparatus for input, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and configured to be executed by the one or more processors comprises instructions for:

In yet another aspect, embodiments of the invention disclose a machine-readable medium having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform an input method as described in one or more of the preceding.

The embodiment of the invention has the following advantages:

the embodiment of the invention searches the multivariate relational data according to the context, and can reduce the corresponding calculation amount of searching compared with the search of the multivariate relational data according to the entry corresponding to the input string, thereby reducing the consumption of equipment resources.

Moreover, in the embodiment of the present invention, the hit element is obtained according to the context, and the first candidate corresponding to the input string is determined according to the target hit element corresponding to the input string, the obtaining process of the first candidate does not limit the word length corresponding to the input string, and even if the word length corresponding to the input string is short (for example, 1), the embodiment of the present invention can still obtain the corresponding first candidate; therefore, the embodiment of the invention can provide the long word association function corresponding to the input string under the condition of reducing the consumption of equipment resources, thereby improving the input efficiency; in addition, the embodiment of the invention can provide the usable long word association for the user under the condition of short input strings, thereby improving the input experience and the input efficiency.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.

FIG. 1 is a schematic illustration of an environment in which an input method of an embodiment of the invention may be used;

FIG. 2 is a flow chart of the steps of a first embodiment of an input method of the present invention;

FIG. 3 is a flowchart illustrating steps of a second embodiment of an input method;

FIG. 4 is a flowchart of the steps of a third embodiment of an input method of the present invention;

FIG. 5 is a block diagram of an input device according to an embodiment of the present invention;

FIG. 6 is a block diagram of an apparatus 800 for input of the present invention; and

fig. 7 is a schematic structural diagram of a server in some embodiments of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The embodiment of the invention provides an input scheme, which can search in multivariate relational data according to a context corresponding to an input string to obtain a hit element corresponding to the context; determining a target hit element corresponding to the input string from the hit elements; and determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string.

In embodiments of the present invention, the context may include: the upper text corresponding to the input string and/or the lower text corresponding to the input string. Alternatively, the context is typically the portion before the input cursor and the context is typically the portion after the input cursor.

According to an embodiment, the above may comprise: last or most recent times of on-screen content. According to another embodiment, the above may comprise: and under the communication scene, communicating the communication content sent by the opposite end. According to yet another embodiment, the above may comprise: and in the communication scene, transmitting the communication content to the correspondent node. It is to be understood that the embodiments of the present invention are not limited to the specific context.

The multivariate relational data may include binary and more than binary relational data. Binary relations, also known as 2-grams, are used to represent the probability of two elements appearing in succession, where in the field of input methods, the elements may include: at least one of words, phrases, letters, numbers, and symbols. In the embodiment of the invention, the binary relation mainly comprises the binary relation of the vocabulary, and other types of binary relations can be referred to each other. More than two-dimensional relationships are used to indicate the probability of two or more elements occurring in succession.

The binary relationship data may include, by position of the vocabulary in the multivariate relationship data: left and right elements, the triple relationship data may include: left, middle and right elements. Therefore, in the embodiment of the present invention, the types of the hits may include: left member, or middle member, or right member.

For example, if the input string corresponds to a context, but not to a context, the type of hit may include: and (4) right element. For example, the above corresponding input strings include: "you have" and "love art", the input string is "h", then the hit element (right element) obtained based on the multivariate relation data may include: "member", "video", "did", wherein the target hit element corresponding to the input string "h" may be "member", and thus a first candidate "member" may be provided.

For another example, if the input string corresponds to a context, the type of hit element may include: a middle element. For example, if the above is "weather", the following is "everywhere good scene", and the input string is "h", the hit elements (intermediate elements) obtained based on the multivariate relation data may include: "good sunny", whereby a first candidate "good sunny" may be provided.

For another example, if the input string corresponds to a context and not to a context, the type of hit may include: and (4) a left element. For example, the following corresponding to the input string includes: "republic," with an input string of "z," the hit (left) derived based on the multivariate relationship data may include: "the people of china", whereby the first candidate "the people of china" can be provided.

According to the embodiment of the invention, firstly, according to the context corresponding to the input string, searching is carried out in the multivariate relational data to obtain the hit element corresponding to the context, then the target hit element corresponding to the input string is determined from the hit elements, and then the first candidate corresponding to the input string is determined according to the target hit element corresponding to the input string.

Moreover, the embodiment of the present invention obtains the hit element according to the context, and determines the first candidate corresponding to the input string according to the target hit element corresponding to the input string, where the obtaining process of the first candidate does not limit the word length corresponding to the input string, and even if the word length corresponding to the input string is shorter (e.g., 1), the embodiment of the present invention can obtain the corresponding first candidate.

The embodiment of the invention can be applied to input method programs of various input modes such as keyboard symbols, handwriting and the like, namely, a user can input characters through the code character string, and the input string can refer to the code character string input by the user. In the field of input methods, for input method programs in, for example, chinese, japanese, korean, or other languages, an input string input by a user may be generally converted into a candidate for a corresponding language. Hereinafter, the description will be mainly given by taking Chinese as an example, and other languages such as Japanese and Korean may be referred to each other. It is to be understood that the chinese input method may include, but is not limited to, a full pinyin, a simple pinyin, a stroke, a five-stroke, etc., and the embodiment of the present invention is not limited to a specific input method program corresponding to a certain language.

Taking the input of chinese as an example, the types of the encoding character string may include: pinyin strings, character strings (such as pencils, etc.). Taking english input as an example, the types of the encoding strings may include: alphabetic strings, and the like.

The input method provided by the embodiment of the present invention can be applied to the application environment shown in fig. 1, as shown in fig. 1, the client 100 and the server 200 are located in a wired or wireless network, and the client 100 and the server 200 perform data interaction through the wired or wireless network.

Optionally, the client 100 may run on a terminal, which specifically includes but is not limited to: smart phones, tablet computers, electronic book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop portable computers, car-mounted computers, desktop computers, set-top boxes, smart televisions, wearable devices, and the like. The client 100 may correspond to a website, or APP (Application).

In practical applications, for the input mode of keyboard symbols, a user may input the input string through a physical keyboard or a virtual keyboard. For example, for a terminal with a touch screen, a virtual keyboard may be set in the input interface to use input of an input string by triggering virtual keys included in the virtual keyboard. Optionally, examples of the virtual keyboard may include: a 9-key keyboard, a 26-key keyboard, etc. Moreover, it can be understood that, in addition to the virtual keys corresponding to the letters, the input interface may also be provided with symbol keys, numeric keys, and function keys such as a chinese-english switching key, or may also be provided with toolbar keys, and it can be understood that the specific keys included in the input interface are not limited in the embodiments of the present invention.

According to some embodiments, the input string may include, but is not limited to: a key symbol or a combination of a plurality of key symbols input by a user through a key. The key symbol may specifically include: pinyin, strokes, kana, etc.

In an embodiment of the invention, the candidates may be used to represent one or more characters provided by the input method program to be selected by the user. The candidates may correspond to the context, or the candidates may correspond to the input string and the context. The candidates may be characters of languages such as chinese characters, english characters, japanese characters, and the like, and the candidates may also be symbol combinations in the form of characters, pictures, and the like. The color text includes but is not limited to a drawing composed of lines, symbols and words, for example, examples of the color text may include: ": p ",": o ",": etc.

Binary relational data can be used to reflect the probability of two vocabulary adjacencies being used. In one aspect, the vocabulary may include language words composed of letters, which may be words, phrases, or phrases composed of letters printed on a keyboard, and may be specifically applied to english, french, german, or the like; on the other hand, the vocabulary may further include a character sequence corresponding to a text language composed of pinyin and/or strokes, where the character sequence corresponding to the text language composed of pinyin and/or strokes may include words corresponding to pinyin, words corresponding to strokes, and the like, and may be particularly applicable to chinese, japanese, korean, and the like.

In an alternative embodiment of the invention, the multivariate relational data can be characterized by a data model. Types of data models may include, but are not limited to: language models, neural network models, and the like. The data model described above may provide P (arbitrary element | context, …), i.e., the probability of an arbitrary element under certain context, etc. From this probability, the hit corresponding to the context can be determined. The corpus used by the data model may include: the corpus under the condition of context, etc., the corpus includes but is not limited to: internet corpus, user chat corpus, user input corpus, and the like.

The mathematical model is a scientific or engineering model constructed by using a mathematical logic method and a mathematical language, and is a mathematical structure which is generally or approximately expressed by adopting the mathematical language aiming at the characteristic or quantity dependency relationship of a certain object system, and the mathematical structure is a relational structure which is described by means of mathematical symbols. The mathematical model may be one or a set of algebraic, differential, integral or statistical equations, and combinations thereof, by which the interrelationships or causal relationships between the variables of the system are described quantitatively or qualitatively. In addition to mathematical models described by equations, there are also models described by other mathematical tools, such as algebra, geometry, topology, mathematical logic, etc. Where the mathematical model describes the behavior and characteristics of the system rather than the actual structure of the system. The method can adopt methods such as machine learning and deep learning methods to train the mathematical model, and the machine learning method can comprise the following steps: linear regression, decision trees, random forests, etc., and the deep learning method may include: convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated cyclic units (GRU), and so on.

Optionally, the conditions of the data model may further include: and inputting the environmental characteristics. In this case, the data model may provide P (arbitrary element | context, input environment feature, …).

In the embodiment of the invention, the input environment characteristics can be used for representing the environment information of the terminal when the user inputs the information. The input environment characteristics can reflect the input intention of the user to a certain extent, so that the relation is established between the input environment characteristics and the input intention of the user, the input intention of the user can be indirectly identified, and the input efficiency of the user is improved.

In practical applications, the input environment features may include various types of features. Optionally, the input environment feature may include: at least one of a temporal environment feature, a location environment feature, a climate environment feature, an application environment feature, and a page environment feature.

Even if the same terminal is in the environment information which is likely to change, the time environment characteristic is a typical example. Therefore, the input environment characteristics of the embodiment of the invention can have real-time performance, and the input environment characteristics corresponding to the input string can be acquired in real time in the input process.

For an input string, its reception time may be taken as a corresponding temporal environment characteristic.

Location information obtained according to an IP (Internet Protocol) address thereof, a GPS (Global Positioning System) of the terminal, or a mobile communication network may be used as a corresponding location environment characteristic.

The input method program as a hosted program can be hosted by any host program and can be invoked by the host program to realize input in the host program, for example, a user can type an input string in the host program and select a candidate item corresponding to the input string to be displayed on a screen. In the embodiment of the present invention, the application program environment characteristic corresponding to the input string may be information of a host program corresponding to the input method program.

Optionally, the application environment characteristic corresponding to the input string may be determined according to the identification characteristic of the current object being served by the input method program, for example, when the input method program is in operation, the GetModuleFilename finds a program path name "C: programfiless microsoft office 1winword.

In this embodiment of the present invention, the application environment features may include: an application identification and/or an application category. For example, "word" is an application identifier, "word" corresponds to an application category that is an office category, and so on. It is understood that one skilled in the art can classify the application programs into corresponding application program categories according to the actual application requirements, for example, examples of the application program categories may include but are not limited to: an instant messaging category, a document category, a search category, a web page category, a shopping category, a travel category, and the like.

The page environment features may be used to characterize the page environment provided by the application or website, and optionally, the page environment may include, but is not limited to: an instant messaging page environment, a document page environment, a mail page environment, a password entry page environment, a game page environment, a search page environment, a travel page environment, a shopping page environment, a social page environment, a movie page environment, a reading page environment, and the like.

Of course, the input environment features of the embodiment of the present invention may include other environment features, such as physical environment features of barometric pressure, altitude, temperature, humidity, etc., in addition to the time environment feature, the location environment feature, the application environment feature, and the page environment feature. Wherein, it is understood that the embodiment of the present invention does not impose limitation on the specific input environment features.

Method embodiment one

Referring to fig. 2, a flowchart illustrating steps of a first embodiment of an input method according to the present invention is shown, which may specifically include the following steps:

step 201, searching in multivariate relational data according to a context corresponding to an input string to obtain a hit element corresponding to the context;

step 202, determining a target hit element corresponding to the input string from the hit elements;

step 203, determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string.

The method embodiment shown in fig. 2 may be executed by a client and/or a server, and it is understood that the specific execution subject of the method embodiment is not limited by the embodiment of the present invention.

In step 201, the context may be matched with the structure field at the corresponding position in the multiple element relationship data to obtain a hit element corresponding to the context. A hit element may refer to a structure field that looks for a hit.

For example, the context includes: the above is matched with the left element in the multivariate relational data, and the obtained hit element can be the right element. As another example, the context includes: the following text may be matched with the right element in the multivariate relational data, and the obtained hit element is the left element. Alternatively, the context includes: the upper and lower parts can be matched with the left and right parts in the multiple relation data respectively to obtain a hit part as a middle part.

In step 202, hits may be filtered to obtain target hits corresponding to the input string.

In an alternative embodiment of the present invention, hits may be matched against the input string to obtain target hits.

According to an embodiment, the determining a target hit element corresponding to the input string from the hit elements may specifically include: and matching the coded character string corresponding to the hit element with the input string to obtain a target hit element corresponding to the input string.

The embodiment of the invention can adopt a character string matching mode, and particularly can match the code character string corresponding to the hit element with the input string.

Optionally, the input string may be a portion of an encoding string corresponding to the target hit. For example, the input string is "h", and the code string corresponding to the target hit is "huiyuan".

Optionally, the input string may be a prefix of an encoding string corresponding to the target hit. The prefix may refer to a portion located at the front. For example, the input string is "hui", and the code string corresponding to the target hit is "huiyuan".

According to another embodiment, the determining a target hit element corresponding to the input string from the hit elements may specifically include: and matching the matching sequence corresponding to the hit element with the matching sequence corresponding to the input string to obtain a target hit element corresponding to the input string.

The matching sequence may be an intermediate structure between the encoded string and the entry that can be used to match the entries in the lexicon. The matching sequence may include: syllable sequence, shape code sequence, number sequence, symbol sequence, etc. It is to be understood that the specific matching sequence is not limited by the embodiments of the present invention.

The embodiment of the present invention may adopt a matching manner of matching sequences, and specifically, match the matching sequence corresponding to the hit element with the matching sequence corresponding to the input string.

For example, the input string is "h", "h" corresponds to the syllable sequence [ h ], the code string corresponding to the target hit is "huiyuan", "huiyuan" corresponds to the syllable sequence [ hui ] [ yuan ], [ h ] is matched with [ hui ], i.e. the concisen syllable can be matched with the corresponding full-spelling syllable.

In an optional embodiment of the present invention, a corresponding encoded string or matching sequence may be stored for a structure field in the multiple relation data, that is, a mapping relationship between the structure field and the encoded string or matching sequence may be stored, so that the encoded string or matching sequence corresponding to the hit element may be determined according to the mapping relationship.

It is understood that the above matching of the hits and the input string is only an example of the determination method of the target hits, and in fact, those skilled in the art may determine the target hits by other determination methods according to the actual application requirements.

In an embodiment of the present invention, the target hit element may be determined according to a conditional probability corresponding to the hit element, so that the quality of the target hit element may be improved, and the conditional probability is used to represent the occurrence probability of the hit element under the context condition. Optionally, a hit element with a conditional probability exceeding a probability threshold may be used as a target hit element, and accordingly, the conditional probability of the target hit element exceeds the probability threshold; the conditional probability is used to characterize the probability of occurrence of the target hit under the conditions of the context.

Alternatively, the conditional probability may be obtained according to the corpus. For example, the corpus may be counted, or the corpus may be trained to obtain the conditional probability. The conditional probability may range from [0,1 ].

The probability threshold can be determined by those skilled in the art according to the actual application requirements, and for example, the probability threshold can be a value of 0.6, etc.

In step 203, the target hit element corresponding to the input string may be directly used as the first candidate corresponding to the input string.

Alternatively, the target hit corresponding to the input string may be further processed to obtain a first candidate corresponding to the input string. For example, further associative processing may be performed on the target hits. For example, the first candidate may be obtained by searching in the multivariate relation data according to the target hit element to obtain a right element corresponding to the target hit element, and according to the target hit element and the right element corresponding to the target hit element.

In summary, the input method according to the embodiment of the present invention searches for the multivariate relational data according to the context, and can reduce the amount of computation required for searching for the multivariate relational data according to the entry corresponding to the input string, thereby reducing the consumption of device resources.

Method embodiment two

Referring to fig. 3, a flowchart illustrating steps of a second embodiment of the input method of the present invention is shown, which may specifically include the following steps:

step 301, searching in multivariate relational data according to a context corresponding to an input string to obtain a hit element corresponding to the context;

step 302, determining a target hit element corresponding to the input string from the hit elements;

step 303, determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string;

with respect to the first embodiment of the method shown in fig. 2, the method of this embodiment may further include:

step 304, determining a sorting parameter of the first candidate according to the conditional probability of the first candidate, the word number of the target hit element and the word number of the entry corresponding to the input string in the word bank;

the conditional probability is used for characterizing the occurrence probability of the target hit under the condition of context;

step 305, determining the position of the first candidate in the candidate list according to the sorting parameter of the first candidate.

The embodiment of the invention can determine the ranking parameter of the first candidate, and determine the position of the first candidate in the candidate list according to the ranking parameter of the first candidate, thereby improving the reasonability of the position of the first candidate.

The candidate list corresponds to the input string, the candidate list typically comprising a plurality of candidates, the candidate list may comprise: the first candidate and the second candidate obtained in fig. 2, and the second candidate may include: and searching the obtained candidates in the word bank according to the input string. The types of lexicons may include: a system word stock, a user word stock, a cloud word stock, or the like. For example, the following second candidate may be found in the lexicon according to the input string "h": "would", "and", "good", "still", etc.

In an alternative embodiment of the present invention, the associated word number corresponding to the first candidate may be determined according to the word number of the target hit and the word number of the entry corresponding to the input string in the lexicon. Alternatively, the number of associated words may be determined according to a difference between the number of words of the target hit and the number of words of the entry corresponding to the input string in the lexicon. For example, if the first candidate is "member" and the number of words of the entry corresponding to the input string "h" in the thesaurus is 1, the number of associated words may be 1.

The embodiment of the invention can punish the conditional probability corresponding to the first candidate according to the number of the association words, and particularly can reduce the conditional probability corresponding to the first candidate according to the number of the association words.

Alternatively, a penalty value corresponding to the number of associated words may be preset, and the reduction value of the conditional probability corresponding to the first candidate may be: a penalty value corresponding to the number of associated words multiplied by the number of associated words. Further, the product may be subtracted based on the conditional probability corresponding to the first candidate to obtain the ranking parameter of the first candidate.

It is to be understood that the above-mentioned subtracting the product based on the conditional probability corresponding to the first candidate is only an alternative embodiment of reducing the conditional probability corresponding to the first candidate according to the number of the association words, and in fact, a person skilled in the art may reduce the conditional probability corresponding to the first candidate according to the number of the association words in other ways according to the actual application requirements, for example, determining a reduction ratio according to the number of the association words, reducing the conditional probability corresponding to the first candidate according to the reduction ratio, and so on.

The ranking parameter of the first candidate may be used to determine the position of the first candidate in the candidate list. Optionally, the first candidate and the second candidate in the candidate list may be sorted according to a sorting parameter. Generally, the larger the value corresponding to the ranking parameter, the more forward the candidate is in the candidate list.

The ranking parameters of the second candidate may include: the word frequency of the second candidate corresponding entry, whether the second candidate corresponding entry is from the user lexicon, and the like. Generally, if the entry corresponding to the second candidate is from the user lexicon, the numerical value corresponding to the ranking parameter of the second candidate is set to be a larger numerical value, so that the second candidate is ranked at the top of the candidate list.

Method embodiment three

Referring to fig. 4, a flowchart illustrating steps of a third embodiment of an input method according to the present invention is shown, which may specifically include:

step 401, according to a context corresponding to an input string, searching in multivariate relational data to obtain a hit element corresponding to the context;

step 402, determining a target hit element corresponding to the input string from the hit elements;

step 403, determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string;

step 404, displaying a candidate list corresponding to the input string; the candidate list may include: the first candidate and the second candidate, and the second candidate may include: and obtaining candidates according to the word stock.

In practical application, the input method can display the candidate list through an interface for a user to select.

Optionally, the candidate list may be presented according to a result of sorting candidates in the candidate list, and specifically, the candidate list may be presented according to a descending order of numerical values corresponding to the sorting parameters.

For a better understanding of the embodiments of the present invention, the input method of the embodiments of the present invention is described herein by a specific example, which may specifically include the following steps:

step S1, determining an input string and a context corresponding to the input string;

the input string is "h", which corresponds to the two above: "you have" love the odd art ".

Step S2, searching a corresponding second candidate in the word stock according to the input string;

for example, the second candidate such as "will", "good" and "still" is found by searching the thesaurus according to "h".

Step S3, according to the above, searching in the multivariate relational data to obtain the corresponding hit element;

by inquiring the multivariate relational data, the two right elements corresponding to the above characters of 'you have' and 'love art' comprise: the front part of the member 0.76, the video 0.13, the Domo 0.09 and the member 0.76 represent the conditional probability behind the entry;

step S4, matching the hit element with the input string to obtain a target hit element 'Member 0.76';

step S5, determining the ranking parameter of the target hit element 'Member 0.76';

the modified conditional probability of 0.76-1 × 0.1 — 0.66 is obtained as the ranking parameter by adding some penalty to the long word, such as "associated word number 1 × penalty value 0.1".

Step S6, determining the order of the candidate list, such as "meeting", "membership", "good" and "still", etc

Step S7, the candidate list is presented in the above order.

It should be noted that, for simplicity of description, the method embodiments are described as a series of movement combinations, but those skilled in the art should understand that the present invention is not limited by the described movement sequence, because some steps can be performed in other sequences or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no moving act is required as an embodiment of the invention.

Device embodiment

Referring to fig. 5, a block diagram of an embodiment of an input device according to the present invention is shown, which may specifically include:

a searching module 501, configured to search in the multivariate relational data according to a context corresponding to the input string to obtain a hit element corresponding to the context;

a target hit element determining module 502, configured to determine a target hit element corresponding to the input string from the hit elements; and

a first candidate determining module 503, configured to determine a first candidate corresponding to the input string according to the target hit corresponding to the input string.

Optionally, the target hit meta-determination module 502 includes:

the first matching module is used for matching the coding character string corresponding to the hit element with the input string to obtain a target hit element corresponding to the input string; or

And the second matching module is used for matching the matching sequence corresponding to the hit element with the matching sequence corresponding to the input string to obtain a target hit element corresponding to the input string.

Optionally, the input string may be a portion of an encoding string corresponding to the target hit.

Optionally, the input string may be a prefix of an encoding string corresponding to the target hit.

Optionally, the conditional probability of the target hit may exceed a probability threshold; the conditional probability is used to characterize the probability of occurrence of the target hit under the conditions of the context.

Optionally, the apparatus may further include:

a sorting parameter determining module, configured to determine a sorting parameter of the first candidate according to a conditional probability of the first candidate, the word number of the target hit element, and the word number of a corresponding entry of the input string in a lexicon; the conditional probability is used for characterizing the occurrence probability of the target hit under the condition of context;

and the position determining module is used for determining the position of the first candidate in the candidate list according to the sorting parameter of the first candidate.

Optionally, the apparatus may further include:

the display module is used for displaying the candidate list corresponding to the input string; the candidate list may include: the first candidate and the second candidate, and the second candidate may include: and obtaining candidates according to the word stock.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

An embodiment of the present invention provides an apparatus for input, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs including instructions for: searching in the multivariate relational data according to the context corresponding to the input string to obtain a hit element corresponding to the context; determining a target hit element corresponding to the input string from the hit elements; and determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string.

FIG. 6 is a block diagram illustrating an apparatus 800 for input according to an example embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.

Referring to fig. 6, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing elements 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operation at the device 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

Power components 806 provide power to the various components of device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 800.

The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice input mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed state of the device 800, the relative positioning of the components, such as a display and keypad of the apparatus 800, the sensor assembly 814 may also detect a change in position of the apparatus 800 or a component of the apparatus 800, the presence or absence of user contact with the apparatus 800, orientation or acceleration/deceleration of the apparatus 800, and a change in temperature of the apparatus 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

Fig. 7 is a schematic diagram of a server in some embodiments of the invention. The server 1900 may vary widely by configuration or performance and may include one or more Central Processing Units (CPUs) 1922 (e.g., one or more processors) and memory 1932, one or more storage media 1930 (e.g., one or more mass storage devices) storing applications 1942 or data 1944. Memory 1932 and storage medium 1930 can be, among other things, transient or persistent storage. The program stored in the storage medium 1930 may include one or more modules (not shown), each of which may include a series of instructions operating on a server. Still further, a central processor 1922 may be provided in communication with the storage medium 1930 to execute a series of instruction operations in the storage medium 1930 on the server 1900.

The server 1900 may also include one or more power supplies 1926, one or more wired or wireless network interfaces 1950, one or more input-output interfaces 1958, one or more keyboards 1956, and/or one or more operating systems 1941, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

A non-transitory computer readable storage medium in which instructions, when executed by a processor of an apparatus (server or terminal), enable the apparatus to perform the input method shown in fig. 2 or fig. 3 or fig. 4.

A non-transitory computer readable storage medium in which instructions, when executed by a processor of an apparatus (server or terminal), enable the apparatus to perform an input method, the method comprising: searching in the multivariate relational data according to the context corresponding to the input string to obtain a hit element corresponding to the context; determining a target hit element corresponding to the input string from the hit elements; and determining a first candidate corresponding to the input string according to the target hit element corresponding to the input string.

The embodiment of the invention discloses A1 and an input method, wherein the method comprises the following steps:

A2, the method according to A1, wherein the determining the target hit element corresponding to the input string from the hit elements includes:

matching the coding character string corresponding to the hit element with the input string to obtain a target hit element corresponding to the input string; or

And matching the matching sequence corresponding to the hit element with the matching sequence corresponding to the input string to obtain a target hit element corresponding to the input string.

A3, the method according to A1, wherein the input string is part of an encoding string corresponding to the target hit.

A4, the method according to A1, wherein the input string is a prefix of an encoding string corresponding to the target hit.

A5, the method according to A1, wherein the conditional probability of the target hit exceeds a probability threshold; the conditional probability is used to characterize the probability of occurrence of the target hit under the conditions of the context.

A6, the method according to any of A1 to A5, wherein the method further comprises:

determining the ordering parameter of the first candidate according to the conditional probability of the first candidate, the word number of the target hit element and the word number of the corresponding entry of the input string in the word stock; the conditional probability is used for characterizing the occurrence probability of the target hit under the condition of context;

and determining the position of the first candidate in the candidate list according to the sorting parameter of the first candidate.

A7, the method according to any of A1 to A5, wherein the method further comprises:

displaying a candidate list corresponding to the input string; the candidate list includes: the first candidate and a second candidate, the second candidate comprising: and obtaining candidates according to the word stock.

The embodiment of the invention discloses B8 and an input device, which is characterized by comprising:

B9, the apparatus according to B8, wherein the target hit meta-determination module comprises:

B10, the apparatus according to B8, wherein the input string is part of an encoding string corresponding to the target hit.

B11, the apparatus according to B8, wherein the input string is a prefix of the encoded string corresponding to the target hit.

B12, the device according to B8, characterized in that the conditional probability of the target hit exceeds a probability threshold; the conditional probability is used to characterize the probability of occurrence of the target hit under the conditions of the context.

B13, the device according to any of B8 to B12, characterized in that the device further comprises:

B14, the device according to any of B8 to B12, characterized in that the device further comprises:

the display module is used for displaying the candidate list corresponding to the input string; the candidate list includes: the first candidate and a second candidate, the second candidate comprising: and obtaining candidates according to the word stock.

The embodiment of the invention discloses C15, a device for inputting, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs configured to be executed by the one or more processors comprise instructions for:

C16, the apparatus according to C15, wherein the determining the target hit element corresponding to the input string from the hit elements comprises:

C17, the apparatus according to C15, wherein the input string is part of an encoding string corresponding to the target hit.

C18, the apparatus according to C15, wherein the input string is a prefix of the encoded string corresponding to the target hit.

C19, the apparatus according to C15, wherein the conditional probability of the target hit exceeds a probability threshold; the conditional probability is used to characterize the probability of occurrence of the target hit under the conditions of the context.

C20, the device of any of C15 to C19, wherein the device is also configured to execute the one or more programs by the one or more processors including instructions for:

C21, the device of any of C15 to C19, wherein the device is also configured to execute the one or more programs by the one or more processors including instructions for:

Embodiments of the present invention disclose D22, a machine-readable medium having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform an input method as described in one or more of a 1-a 7.

Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

The present invention provides an input method, an input device and a device for inputting, which are described in detail above, and the principle and the implementation of the present invention are explained herein by applying specific examples, and the description of the above examples is only used to help understand the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. An input method, characterized in that the method comprises:

2. The method of claim 1, wherein the determining a target hit from the hits comprises:

3. The method of claim 1, wherein the input string is part of an encoding string corresponding to the target hit.

4. The method of claim 1, wherein the input string is a prefix of an encoding string corresponding to the target hit.

5. The method of claim 1, wherein the conditional probability of the target hits exceeds a probability threshold; the conditional probability is used to characterize the probability of occurrence of the target hit under the conditions of the context.

6. The method according to any one of claims 1 to 5, further comprising:

7. The method according to any one of claims 1 to 5, further comprising:

8. An input device, comprising:

9. An apparatus for input, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for:

10. A machine-readable medium having stored thereon instructions, which when executed by one or more processors, cause an apparatus to perform an input method as recited in one or more of claims 1-7.