Patent Grant
7945438
The present invention relates to glossaries, and more particularly relates to automatically creating glossaries by utilizing rules to identify which words are likely to be parts of phrases.
Electronic data is available from a myriad of sources. The data can be structured, as is the case with coded fields and timestamps, or unstructured, as found in free-form open language text fields (such as those often labeled “comments” or “description”).
Rule-based systems, data mining systems, data analytics systems, and many other systems work on structured data. However, there is important information in unstructured data, and processing and analysis of this information can provide great business value. To enable this processing and analysis, the unstructured data needs to be converted into the structured type of data on which these systems work.
A glossary is a collection of specialized terms or phrases and their meanings. Many methodologies for processing text documents and fields are based on using such glossaries. For example, one use for glossaries is in processing (and performing text analytics on) post-sales product data and failures, such as warranty claims and field service reports. The glossaries are used to pull out symptoms, causes, actions, and components from failure reports.
In many instances, the size of a glossary can be extremely large. For instance, in the area of automotive warranty claims, a car has several thousand parts and each part typically has several types of failures. Further, failures can be caused by interdependencies between different parts. While this creates a very large number of entries, this number significantly increases when one considers variation in language to describe these failures. This is due to the fact that there are many ways to say the same thing, even in perfectly written English. Factoring in typographical errors, improper grammar, abbreviations, and other factors adds further complexity.
It is impractical for a human to manually create all of the possible variations of each canonical form, let alone create all the possible canonical forms. There are procedures that assist in manual creation, by partially automating some of the glossary creation process.
The present invention provides a method for creating a glossary, which includes the steps of identifying, in at least one information source, at least one glossary item identifying a part or a component, determining at least one glossary item form as a canonical form, defining, by using the canonical form, at least one syntactic structure, that includes one of the at least one identified glossary items, for each of at least one semantic classes, and searching an information source for the syntactic structure of the semantic class.
In accordance with another feature, the present invention includes determining at least one glossary item form as a variant form, then defining, by using the variant form, at least one syntactic structure, that includes one of the at least one identified glossary items, for each of at least one semantic classes, and finally, searching an information source for the at least one variant form of the syntactic structure of the semantic class.
In accordance with a further feature of the present invention, the semantic class is at least one of a symptom, a cause, and an action.
In accordance with an additional feature of the present invention, the determining is performed by mapping variant glossary item forms onto canonical forms.
In accordance with an added feature of the present invention, the defining is performed by parsing to identify the at least one syntactic structure typical of a semantic class.
In accordance with yet another feature, the present invention includes defining, by using the canonical form of the glossary item, at least one syntactic structure, that does not include one of the at least one identified glossary items, for each of at least one semantic classes.
The present invention also provides a device for creating a glossary, which includes a processor operable for executing computer instructions for identifying at least one glossary item in a first information source, determining at least one glossary item form as a canonical form, defining, by using the canonical form, at least one syntactic structure, that includes one of the at least one identified glossary items, for each of at least one semantic classes, and searching a second information source for the at least one syntactic structure of the semantic class.
In accordance with an added feature, the processor is further operable to execute computer instructions for determining at least one glossary item form as a variant form and defining, by using the variant form, at least one syntactic structure, that includes one of the at least one identified glossary items, for each of at least one semantic classes.
The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views and which together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the present invention.
While the specification concludes with claims defining the features of the present invention that are regarded as novel, it is believed that the present invention will be better understood from a consideration of the following description in conjunction with the drawing figures, in which like reference numerals are carried forward.
Preferred embodiments of the present invention provide an apparatus and method for automatically creating glossaries that can then be used to extract detailed information (such as symptoms, causes, actions, and other valuable information) from specialized resources (such as warranty claims, field reports, call center records, and customer surveys). This detailed information can then be used for early warning detection as well as data mining, including trend analysis and failure prediction.
One exemplary application for an embodiment of the present invention is in analyzing failure reports, and in particular, failure reports that also include information about the repair. Failure reports can take many forms including warranty claims, call center records, field engineer service reports, and others. These failure reports typically contain structured fields (such as product identification, repair date, and repair costs), as well as coded fields (such as those that describe the failure and symptom at a high level). Additionally, there typically exists an unstructured text field for a comment or description. Repair technicians typically type up the reported problem (i.e., the symptom), what they found (i.e., the cause), and what they did to fix the problem (i.e., the repair action) in these unstructured text fields. There may be other useful information in the text fields such as the problem and replaced parts, diagnostic actions, and test actions. While analyzing failure reports is an exemplary application, the present invention can be used in many applications and is in no way limited to only those examples described herein.
While symptoms, causes, and actions are often represented in coded fields of the failure reports, these are typically only high-level codes that do not contain the same richness of information that appears in the text of the comments placed in the unstructured fields. Embodiments of the present invention use text analytics on the comments in an unstructured text field to convert this rich information into structured information.
The simplest glossary-based technique is to look for occurrences of words and phrases. For instance, algorithms are used to derive the noun to which a pronoun refers. To apply this technique, incoming failure reports are scanned for occurrences of the phrases contained in the glossary with the goal of pulling out one or more symptoms, causes, and actions. Then, analytics are applied to those (structurally) tagged failure reports.
A portion of a sample subset of a glossary is shown in
After failure reports, or other data sources, are tagged with their occurrences, analytics are applied based on the number of occurrences of each type of symptom, cause, and action, and each occurrence's attributes such as failure time, failure location, and other coded fields. This exemplary embodiment of the present invention uses the Systems Modeling Language (SysML), a standard language for describing systems and complex products, for modeling systems, describing elements of the system, and the relationships between these elements. The language has a visual counterpart and is supported by modeling environments such as the Rational Software Architect.
For example, a sample portion of ISO STEP AP233 XML data is the following.
In this sample data, the contents of the “Name” tag (i.e., “Master Cylinder”) identified within a “Part” tag can be extracted. From this simple extraction, a part called “Master Cylinder” is identified. In further embodiments, other methods are used to perform the extraction of data from the system model, such as the use of the XML Path Language (XPath), for example with the XPath expression “//ap233:Part/@id”. It should be noted that the invention is not limited to any particular programming language(s).
Besides structured sources such as the SysML model of
1) a complaint statement labeled “Complaint”;
2) a possible cause labeled “Potential Problems”; and
3) a set of diagnostic and corrective actions in list format labeled “Solution”.
From this formatted text document, rules can be devised to make a source-specific pattern adapter for the Technical Service Bulletin. An automated process is then able to use the rules to parse the text document and extract useful information. For example, one rule is that the “Complaint” statement defines a symptom. Using this rule, when the automated process finds the pattern “Complaint” in the text document, a symptom is then extracted. Further rules can similarly be used to extract causes and actions from the “Potential Problems” and “Solution” sections of the Technical Services Bulletin. Thus, the rules form a pattern adapter for this specifically formatted text data source.
As shown, the parts and components extraction is performed with a set of adapters (such as a system model adapter 702, a parts catalog adapter 704, a repair manual adapter 706, a technical service bulletin adapter 708, and a bill of materials adapter 714), which are each specific to a particular data source. More specifically, in this exemplary embodiment the system model adapter makes use of XML tags (e.g., through XPath) to extract a hierarchical tree of components and their products from the system model 702 (e.g., in the manner described above with reference to
Thus, structured sources such as the system model, semi-structured sources such as Technical Service Bulletins, and unstructured text sources such as the repair manual are all used to derive glossary items. In order to use the unstructured sources in creating the glossary, syntactic analysis of each sources' unstructured text contents are performed and combined with the glossaries derived from the more structured sources.
When this information is extracted from so many sources, virtually every extracted item will have a canonical form and variants. The “canonical form” is the phrase that represents the most common way of stating a particular concept, and each “variant” is an alternate phrase for specifying the same concept. One example is the action “replace wiper blade” as a canonical form that has a variant “put on new wiper blade.” The same is true for causes and symptoms that are extracted. Preferably, the variants are grouped together with the canonical form, as shown in
A process flow diagram of an automated glossary extraction process that can arrive at a structure such as that shown in
Next, in step 804, glossary tools are used to map variant glossary item forms onto their canonical (“common”) forms. Step 804 can use glossary tools of two types. First, automatic tools, such as the “glossary extraction” tool, can identify variants of the same glossary item and can select a canonical variant from among them. Such a tool handles, for example, variants that are misspellings, alternate spellings, abbreviations, acronyms, compound words, alternate phrasings, or that differ in the use of character case or special characters. A second type of tool is a “glossary administration” tool, with which a domain expert can inspect and modify candidate glossary items from any source.
In step 806, “shallow parsing” is used to identify syntactic structures typical of semantic classes. “Parsing” refers to an analysis of text that determines the structural relationships among the parts of the text. For example, in the text “replaced the exhaust manifold”, the verb “replaced” takes the noun-phrase “the exhaust manifold” as its direct object. In this example, the act of parsing determines that the entire text is a “verb-phrase” containing the syntactic categories “verb” and “noun-phrase”, with the relationship “direct object” linking them. “Shallow parsing” is a particular style of computerized parsing, in which the parser can quickly determine the structure of local regions in the text, but may not accurately portray the grammatical structures of full sentences. Such parsers are rule-driven and contain rules such as the following:
To assist the parser, a semantic lexicon (for example, built from the glossaries developed by the present invention) contains words and phrases that are used in these syntactic structures. In the example rules above, “<action-verb>”, refers to entries in the lexicon, such as “replace”, which designate an action. Similarly, “<component-np>” refers to noun-phrase entries which can be a component of an automobile, such as “exhaust manifold”. “<non-component-NP>” refers to a syntactic structure which is a noun phrase (as determined by the parser), but which is not listed in the lexicon as a component. Finally, the underlined portion of the rule indicates which part of the match text is to be given the semantic label that the rule assigns. For example, the first rule marks the entire verb-phrase as an ACTION.
Using the shallow parsing with the paragraph of
In step 808, semantic classes are assigned to failure reports by assigning semantic classes to the glossary items and doing lexical lookup for the text in the reports and/or using the parsing rules to assign further semantic classes to phrases and clauses in the reports. The rules leverage parts of speech in a dictionary 712 to help identify which words are likely to be parts of phrases for components and similarly for each symptom, cause, and action. The process ends at step 810.
A “glossary extraction” process, according to an embodiment of the present invention, shown schematically in
Based on occurrences of these candidate items in the text, on their co-occurrences with possible variant forms, on their syntactic shape (e.g., singular/plural forms), and on their occurrence statistics, automatic processes group the variants into glossary items and select the “common” variant in each item in step 916. These candidate items and variants are stored in a glossary 918 in step 920.
Then, in step 922, using these candidate items as starting points, further parsing can look for phrases and clauses which include them and which have syntactic shapes typical of symptoms, causes, and actions. For example, candidate ACTIONs can be found by looking in the text related to actions for verb phrases consisting of an action-verb followed by a component-noun-phrase (or for a passive-verb-phrase or adjectival-noun-phrase equivalent). Similarly, SYMPTOMs could be identified by parsing the text related to symptoms for configurations of a report-verb followed by either a clause or by a non-component-noun-phrase. In this case, the clause or noun-phrase would become the SYMPTOM. In certain unstructured document resources, the accuracy of such rules can be enhanced by limiting their application to sections of the documents containing the reported speech of the customer.
In one embodiment of this invention, the set of parsing rules used for this analysis will be specially built for the product domain, the available document sources, and the semantic classes (e.g., ACTION, SYMPTOM, and CAUSE) required. In addition to providing the auxiliary structured and semi-structured sources used to bootstrap the analysis with nominal expressions, the preparation will also involve creating other resources, such as the lists of action-verbs and report-verbs mentioned above.
In further processing, glossary extraction contains algorithms for determining variant forms of the new glossary items and for choosing their canonical forms and grouping variants of common causes in step 924. Once the resulting items are reviewed and stored in a glossary, or “semantic dictionary”, in step 926, they can be used for subsequent analysis of product failure reports, as shown in
The hardware platform includes a computer system.
Generalized Architecture for a Computer System
The computer system can include a display interface 1008 that forwards graphics, text, and other data from the communication infrastructure 1002 (or from a frame buffer not shown) for display on the display unit 1010. The computer system also includes a main memory 1006, preferably random access memory (RAM), and may also include a secondary memory 1012. The secondary memory 1012 may include, for example, a hard disk drive 1014 and/or a removable storage drive 1016, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. Removable storage drive 1016, reads and writes to a floppy disk, magnetic tape, optical disk, etc., storing computer software and/or data. The system also includes a resource table 1018, for managing resources R1-Rn such as disk drives, disk arrays, tape drives, CPUs, memory, wired and wireless communication interfaces, displays and display interfaces, including all resources shown in
In alternative embodiments, the secondary memory 1012 may include other similar means for allowing computer programs or other instructions to be loaded into the computer system. Such means may include, for example, a removable storage unit 1022 and an interface 1020. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 1022 and interfaces 1020 which allow software and data to be transferred from the removable storage unit 1022 to the computer system.
The computer system may also include a communications interface 1024. Communications interface 1024 allows software and data to be transferred between the computer system and external devices. Examples of communications interface 1024 may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via communications interface 1024 are in the form of signals which may be, for example, electronic, electromagnetic, optical, or other signals capable of being received by communications interface 1024. These signals are provided to communications interface 1024 via a communications path (i.e., channel) 1026. This channel 1026 carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, and/or other communications channels.
In this document, the terms “computer program medium,” “computer usable medium,” and “computer readable medium” are used to generally refer to media such as main memory 1006 and secondary memory 1012, removable storage drive 1016, a hard disk installed in hard disk drive 1014, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as Floppy, ROM, Flash memory, Disk drive memory, CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network, that allow a computer to read such computer readable information.
Computer programs (also called computer control logic) are stored in main memory 1006 and/or secondary memory 1012. Computer programs may also be received via communications interface 1024. Such computer programs, when executed, enable the computer system to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable the processor 1004 to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.
Although specific embodiments of the present invention have been disclosed, those having ordinary skill in the art will understand that changes can be made to the specific embodiments without departing from the spirit and scope of the present invention. The scope of the present invention is not to be restricted, therefore, to the specific embodiments. Furthermore, it is intended that the appended claims cover any and all such applications, modifications, and embodiments within the scope of the present invention.
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