Patent Application
20070094183
People currently use computers for many different tasks. One common task is related to information retrieval in which a user poses a query to a search engine or help system to obtain desired information. In many cases, the user needs to search for information where the level of expertise the user possesses in particular domains affects the user's satisfaction with the returned results. However, people have different experience levels or levels of expertise in different domains. For example, computer users have a wide variety of knowledge in domains such as computers, medicine, or legal professions among others.
This can present problems in retrieving information relevant to a user's query and other problems as well. For example, if the user is a novice in a particular domain and the computer returns complex or advanced material in response to a query that was not intended to be complicated, the user will be confused. Similarly, if the user has a high degree of expertise and the information returned is rudimentary, the user may become frustrated.
In addition, less experienced people may find it difficult to use appropriate domain-specific language and formulate questions outside of their area of expertise. This is due in part to the non-expert's lack of familiarity with domain-specific technical terms (or jargon) and the proper use of this jargon. A user's lack of knowledge of jargon or domain-specific vocabulary can frustrate information retrieval because of mismatches between the non-experienced user's query expression and the language used or expressed in expert documents or publications.
Ascertaining a user's level of expertise in a particular domain or area is generally difficult. This has conventionally been done using subjective assessments, such as questionnaires. Relying on a user's own assessment of expertise may not be accurate since people often misrepresent their level of expertise or can overlook an area of expertise they may have forgotten.
The discussion above is merely provided as general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.
A system and model is used to determine a level of a user's expertise in a particular domain. In the embodiments described, the expertise model is generated by extracting jargon-based features from a training text corpus. A model training component uses the extracted features to generate the expertise model. The expertise model can be used for varied applications such as determining a user's level of expertise in association with providing help resources in response to a user help inquiry, ordering or re-ranking query results for information retrieval, or identifying subject matter experts, among other applications.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter
The present application relates to user modeling. Prior to describing it in great detail one embodiment of an environment in which it can be used will be described.
The computing system environment 100 shown in
The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Those skilled in the art can implement aspects of the present invention as instructions stored on computer readable media based on the description and figures provided herein.
The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
With reference to
Computer 110 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 110 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.
Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 100. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier WAV or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, FR, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media.
The system memory 130 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 131 and random access memory (RAM) 132. A basic input/output system 133 (BIOS), containing the basic routines that help to transfer information between elements within computer 110, such as during start-up, is typically stored in ROM 131. RAM 132 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 120. By way of example, and not limitation,
The computer 110 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,
The drives and their associated computer storage media discussed above and illustrated in
A user may enter commands and information into the computer 110 through input devices such as a keyboard 162, a microphone 163, and a pointing device 161, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 120 through a user input interface 160 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor 191 or other type of display device is also connected to the system bus 121 via an interface, such as a video interface 190. In addition to the monitor, computers may also include other peripheral output devices such as speakers 197 and printer 196, which may be connected through an output peripheral interface 190.
The computer 110 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 180. The remote computer 180 may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 110. The logical connections depicted in
When used in a LAN networking environment, the computer 110 is connected to the LAN 171 through a network interface or adapter 170. When used in a WAN networking environment, the computer 110 typically includes a modem 172 or other means for establishing communications over the WAN 173, such as the Internet. The modem 172, which may be internal or external, may be connected to the system bus 121 via the user-input interface 160, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer 110, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,
System 200 uses a training text corpus 210 for model generation. The training text corpus 210 for example, can include domain specific reference texts, documents, publications and/or natural language queries. For instance, in the computer domain, corpus 210 might include books, computer manuals, help screens for one or more operating systems, text generated at experts' websites, newsgroups, queries to experts or help systems or any other input text that has a labeled or ascertainable expertise level. Labels may be obtained through human transcription, automatic transcription, or may be inherent in the text itself, such as the organizational structure of the text (e.g., Table of Contents).
In the embodiment shown, the expertise model is generated based upon jargon-based features. Therefore, training text corpus 210 is provided to jargon identifier component 204 that extracts jargon terms 212 from the training text corpus 210. The jargon terms 212 and training text or corpus 210 are provided to the feature extractor 206 to extract jargon-based features 214. The jargon based features 214 are provided to model training component 208 to generate or train the user expertise model 202, which can be used to determine a level of user expertise based on a textual input.
In an illustrative example, the comparative text includes natural language queries which can be collected or amassed from postings of domain specific newsgroups. In a computer domain embodiment, natural language queries can be collected from newsgroup postings relating to operating systems help and support.
The postings can be selected to provide both novice and expert queries. For instance, the postings are categorized relative to experts vs. non-experts to facilitate comparison between an expert's use of jargon and a novice person's use of jargon. The expert's text in the posting serves the same function as the canonical text to provide a comparison relative to proper or expert jargon usage.
Experts or expert postings can be distinguished based upon an expert designation known in the industry (such as Most Valued Professionals (MVP) in the computer industry). Some experts can be identified based upon a known designation in their e-mail address. Postings can also be categorized based upon first-in-thread vs. non-first-in-thread. First-in-thread refers to the initial query thread and the non-first-in-thread refers to a reply or response to the first-in-thread. Novice users tend to initiate query threads and expert users tend to respond. Postings can be categorized based upon queries vs. solutions. Queries and solutions can be segregated based upon phrases in the queries such as “How do you . . . ?” or “Have you tried . . . ?”
As indicated by block 222 in
Features 214 extracted from the postings and canonical text are provided to the model training component 208 along with a categorization of expertise to generate the expertise model 202. The particular features extracted by extractor 206 can vary widely, and one embodiment is described in greater detail below with respect to
As mentioned, the particular features extracted by feature extractor 206 can vary widely. In one embodiment feature extractor 206 extracts features relating to the semantic relation of the jargon terms relative to other words in the natural language input or training corpus 210. To extract semantic based features, the syntactical structure of the natural language input is analyzed.
As shown in step 240, sentences are identified in the training text corpus 210. A natural language parse of the identified sentences is performed to obtain syntactic parse trees and logical forms as illustrated by step 242. An example embodiment of a natural language parser to form syntactic trees and logical forms from an input sentence in training text corpus 210 represents a predicate-argument relation in a semantic graph, although application is not limited to a particular parser. Generating logical forms can be done in any desired way such as that set out in U.S. Pat. No. 5,966,686 entitled “Method and System for Computing Semantic Logical Forms From Syntax Trees”.
Once the logical form is generated for the input training sentences, n-tuples (such as triples) are extracted which contain the jargon terms. This is indicated by step 244. In an illustrated example, logical form triples are extracted in the structure <object1, relation, object2>, where the relations are represented by labels on the arc in the logical form and either object1 or object2 is the jargon term.
A feature is selected as illustrated by step 246 for the input training text or query 210. For example, a feature such as (*,Tobj, jargon term) is selected based upon the n-tuples or triples for the input text or query. For the selected feature, a model feature is generated based upon a relationship of the extracted tuple or triple relative to expert or canonical text. This is illustrated in step 248.
The model feature is generated in a wide variety of ways. For instance, a comparative feature may be generated based on whether the expert text includes the selected jargon based feature. For example, for the input text “run the Internet”, the fact that the selected jargon based feature is not found in the canonical corpus becomes a comparative feature. In another embodiment, the feature includes a weight assigned based upon a distribution of the extracted tuple or triple or selected feature in the canonical corpus or expert text.
Alternatively, the weight can be assigned simply based on a frequency of occurrence of the matching tuple in the canonical corpus or expert text. The weight can also be assigned based on a source of the matching tuple. For example, a weight can be assigned based upon whether the matching tuple or jargon-based feature was derived from advanced topics or higher level publications directed to higher level expertise. Also, if the extracted tuple appears in Chapter 15 of a resource text rather than in Chapter 1, the weight assigned may reflect a more expert level, given that the resource text is ordered in the difficulty of the concepts discussed.
In any case, the feature is used by the model training component 208 to build the user expertise model 202.
Multiple features can be selected or generated for the input text or query as illustrated by line 250. For example, multiple features can be selected for different jargon terms and/or relations. Feature selection and processing continues until no more features need to be processed. If there are no more potential features, then a set of features is output as illustrated by step 252.
An example may help to illustrate this embodiment. Assume a training sentence is the query, “How do I run the Internet on operating system XYZ?”. The sentence is parsed to obtain syntactic parse trees and logical forms. In an example embodiment, the triple <run, Tobj, Internet> for the jargon term “Internet” is generated from the logical form for the sentence. Tobj is a “typical object” relation. The feature <*,Tobj, Internet> is selected and a weight is assigned to the extracted triple <run, Tobj, Internet> based upon the relationship of the extracted triple relative to the selected feature in the canonical corpus.
In other words, in the illustrated embodiment, the extracted triple <run, Tobj, Internet> is compared to the distribution of triples with the selected feature <*,Tobj, Internet> in the canonical corpus. In this example, for the selected feature <*,Tobj, Internet>, the word “access” is the most frequently occurring first object of that triple—e.g. it corresponds to “access the internet”. The triple <run, Tobj, Internet> does not appear at all. Hence in the illustrated example, a weight that reflects that <run, Tobj, Internet> does not appear in the training text or canonical corpus 210 is generated. In contrast if the triple does appear, its weight would reflect its occurrence with respect to other triples for the selected feature <*, Tobj, Internet>.
Of course, as discussed above, other features can be selected or the model features can be derived in other ways, such as use of a jargon term in a particular source or particular context. For example, if the query includes a jargon term that appears in a highly advanced or complex document, the training component 208 may assign a higher weight than if the jargon term appears in an elementary text. As discussed above, the model feature can be a differential feature that assesses differences between a canon feature and a comparative text feature. In the above-example, if the comparative text has a jargon-based feature—“run the Internet”, but the canon text does not have such an instance, then the fact that it does not have that instance is the model feature.
Component 302 uses the user expertise model 202 to determine the user expertise level 300 based upon user input 304. This is illustrated in block 344 of
In another embodiment, expertise level 300 can be provided as input to a post processing component 310 for various applications.
In one embodiment, the processing component 310 uses the user's expertise level 300 to provide more appropriate or supplemental help support 312 from a help resource store 314 in response to the user input query 304. This allows a novice user to receive help that is directed at a level which the user can understand. Conversely, a more experienced user can receive resource information tailored to a more expert level. Accessing a help resources data store 314 and providing supplemental help resources 312 is shown in blocks 348 and 350.
In another embodiment, the post processing component 310 uses the user's expertise level 300 to order query results or search results 320 retrieved in response to the user input query 304. As shown in
Of course, other post processing can be performed as illustrated by block 356.
Another application uses a user expertise level to respond to a search query with an elaboration question, declarative answer or request for clarification. In particular in response to a search or help query, a search engine can provide not only a result list but also respond with an elaboration question or declarative answer. For instance for the question “What is cat”, or search term “cat” the search engine can request additional information or respond in the form of a question, such as “Did you mean animal CAT or the UNIX command CAT?”.
In the another application, the processing component uses the user's expertise level to provide an expertise based query response or request additional information in the form of a question as illustrated by block 320. For example, the processing component can use the user expertise level among other features to determine that the user is asking a technical question. Thus, based upon the user's expertise level in the computer domain, the system can respond to the user's question “What is CAT” with the result that “CAT stands for ‘concatentation’ and is used to append files”. Alternatively if the user does not have expertise in the computer domain, the system can respond by requesting additional information or requesting clarification such as, “Did you mean animal CAT or UNIX command CAT?”.
Application of the user expertise -model disclosed is not limited to the embodiments illustrated in
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
