Perspectives on Building Embedded Conceptual Parsers

What we have described in this dissertation owes much to other lines of research, and so it is important to discuss related research and attempt to bring together several theories of building embedded parsing. We do this by discussing two broad areas of research that have a direct bearing on indexed concept parsing and the types of embedded conceptual parsers discussed in this work: other conceptual parsers and statistically-based information retrieval. After discussing these areas, we attempt a synthesis to create a unified architecture for building embedded conceptual parsers for interactive domains.

Having described related work, and a unified architecture, we describe possible extensions to other natural language engineering tasks, and what software tools could be developed to embody the unified architecture.

The history of conceptual parsers

Indexed concept parsing is one in a series of parsing architectures based on the premise that the nature of a natural language understanding system is primarily semantic. In the first chapter, we describe the lineage of conceptual parsers from which indexed concept parsing is derived. This lineage includes parsers based on the Conceptual Dependency (CD) theory of knowledge representation (Schank & Abelson 1977), including Spinoza and Spinoza II (Schank, et al. 1970), MARGIE (Riesbeck 1975), ELI (Riesbeck & Schank 1976) and CA (Birnbaum & Selfridge 1981). As parsing became entwined with investigations into the larger issues of story understanding, the CD parsers were used in such systems as SAM (Schank & Abelson 1977), PAM (Wilensky 1978), and FRUMP (DeJong 1979). Later research focussed more and more on memory and inference, and with the development of Schank's model of Dynamic Memory (Schank 1982) a number of new parsing and inference systems, including IPP (Lebowitz 1980), BORIS (Dyer 1983) and MOPTRANS (Lytinen 1984) were developed. In contrast to these build and store systems (that is, parsers that build new conceptual representations and store them in memory--Riesbeck, 1986), DMAP (Direct Memory Access Parsing; Martin 1989, 1990) is a model of parsing that attempts to model parsing as largely a recognition task. But as we stated in the first chapter, issues of memory and inference, and eventually issues surrounding the building of large scale, practical systems began to dominate, and parsing was largely ignored. Rather, parsers are to be built to serve practical goals; the sort of parsers we have described in this work.

Information retrieval and parsing

Information retrieval systems (Croft 1990; Hayes 1990; Salton & McGill 1983; Salton 1988; Sparck Jones 1990) are usually developed with the following task in mind: Given a query, and a set of documents, which documents best match the query? Typically, a set of key words or phrases, or index terms, is assigned to each document; a query is a set of these index terms which is matched to the index terms assigned to documents, and the best matching documents are returned as the result of the query.

Superficially, at least, the architecture for information retrieval is very similar to indexed concept parsing, which is built with the following task in mind: Given a query, and a set of target concepts, which target concepts best match the query? A set of index concepts is assigned to each target concept; parsing is used to convert the query into a set of index concepts. This set of index terms is matched to the index concepts assigned to target concepts, and the best matching target concepts are returned as the result of the query.

Differences between information retrieval and indexed concept parsing

The main differences between information retrieval technologies and indexed concept parsing include the object of a query, the purpose of a query, what composes a query, the addition of a parsing step in indexed concept parsing, and the matching function used.

The object of a query in text retrieval is usually a document or other information bearing object (IBO); the object of a query in indexed concept parsing is a target concept. An IBO is an object that is external to the query mechanism; a target concept is an internal representation. A major implication of this difference is that internal representations of IBOs must be created for text retrieval; because IBOs (such as documents, videos, etc.) can be very complex to represent, the effort has been on cheap, automatic indexing based on the surface forms found in an IBO, such as the words of a document or the shapes of images in a video. Because target concepts can possibly be in the same representational language that is used by an indexed concept parser, advantage of this knowledge can be taken, as described in Chapter 5-7.

The purpose of a query in information retrieval systems and the purpose of a query for an application using indexed concept parsing will typically differ. Usually, an information retrieval system places very few constraints on the purpose of a query--which makes taking advantage of such constraints a difficult task. On the other hand, we have built indexed concept parsers for embedding into interactive dialog systems, where the goal is somewhat clearer--finding the closest match to a target concept to which the program knows a reasonable response. We have taken advantage of this constraint to bias the parser towards target concepts expected in a particular situation (see Chapter 6).

Indexed concept parsing provides a parsing step between the query and the matching steps, which most information retrieval systems don't provide. A parser allows a richer representation of the index concepts--index terms in information retrieval systems are typically just the surface forms, but index concepts can contain hierarchical and partonomic structure. Furthermore, different ways of phrasing the same index concept--which we'll call synonym sets, although the patterns can contain more than just individual words--is explicitly allowed in indexed concept systems, while in most information retrieval systems this is absent or implicit.

Finally, the match step in information retrieval systems is different from the match step in indexed concept parsing. Typically, information retrieval systems only use the type of appraiser that we have called the predicted score; that is, positive evidence that an IBO is relevant is accrued only in the case when an index term in the query is associated with the IBO. Indexed concept parsing provides other appraisers (such as unpredicted score and unseen score) as described in Chapters 5-7. This is related to the other differences we have mentioned, in that the complicated nature of the informational structure of an IBO, and the goals a user might have in searching for one, means the method of calculating the goodness of a match must almost necessarily be a weak one, and the predicted score provides just such a weak method.

Having described these differences, it must be admitted that there are a good many similarities between information retrieval systems and indexed concept parsing. In fact, information retrieval systems can be seen as a type of indexed concept parsing system--an idea we develop below. But first, we should ask the question, why not build a parser using index terms?

Why not a parser built using index terms?

There are attractions to automatic indexing, which is that the development costs are lower and that the indexing is done very consistently (that is, mechanically). Considering the indexed concept parser built for the Casper customer service representative, for example, it would be less expensive to generate the index concepts from the text of the target concepts (the CSR utterances), rather than require that a content specialist (knowledge engineer) assign index concepts to the target concepts.

Two experiments were run, creating index concepts automatically, and the results compared against the systems described in Chapters 5 and 6. The first text retrieval experiment used weighted, stemmed lexical items as index terms. The second experiment used weighted, stemmed lexical bigrams as index terms. By stemmed index terms we mean that the index terms were reduced to a canonical morphological root. To do this, we used data from the WordNet system (Miller et al. 1990) and a simple morphological stemmer based on this data to reduce each term to its stem. Because the data were relatively sparse, it seemed that an index term approach would be more successful with stemming than with the non-stemmed forms. So, in the lexical item experiment, the text of the target concept ASK-PROBLEM-DURATION, "How long have you had the problem?" resulted in the set of lexical terms {"how" "long" "have" "you" "the" "problem"}.

By lexical bigrams we mean that the lexical items in the input were taken two at a time. So, in the lexical bigram experiment, the text of the target concept ASK-PROBLEM-DURATION, "How long have you had the problem?" resulted in the set of lexical terms {"how long" "long have" "have you" "you have" "have the" "the problem"}.

The weights on an index term in both experiments were set as the information value (as defined in Chapter 5) of the index term; that is, , where i is the number of times the index term is assigned to target concepts, and n is the number of target concepts. The index concept parsing algorithm was used for determining matches; only the predicted items appraiser was used, as is typical in text retrieval systems.

Table 9.1 shows the results of the experiments. The table shows a comparison between the index term systems and the index concept parsing systems described in Chapters 5 and 6. There are two columns under indexed concept parsing; the first shows the results of the first testing of the indexed concept parser; the second column shows the results of revising the knowledge representations, in which additional knowledge representation was done to create or change phrasal patterns, index concepts, or target concepts.

Results comparison: Index term systems vs. indexed concept parsing

Index term systems

Indexed concept parsing

Key words
Basic KR[1

]Additional KR
% Perfect
% Acceptable
Average Distance
Between the two index term systems, the key word system performed better on all measures. However, even the first attempt at indexed concept parsing performed better than either index term system, by at least 10% on both recall measures. Why did indexed concept perform better?

An explanation suggests itself when the revised indexed concept parsing system is compared to the basic indexed concept parsing system. One notes that the additional knowledge representation done on the indexed concept parsing system produced improved results. At least for this application, it suggests that a major advantage of indexed concept parsing over key word systems is productive additivity, that is, the more knowledge added to the system, the better the system performs. Parsing systems based on statistical approaches can only perform as well as the laws of large numbers will take them. In fact, indexed concept parsing can be viewed as the addition of knowledge representation to the surface forms of index terms, by adding synonymy and structured knowledge representations.

A unified architecture for embedded conceptual parsing in interactive domains

The discussion of conceptual parsers and information retrieval systems brings us to make the following claim: the architecture for indexed concept parsing provides a unified architecture for conceptual parsing in interactive domains, which spans the continuum from key word systems to conceptual parsers. It allows us to define five stages of index concept parsers, on a continuum which describes the complexity of the index concepts.

Stages of complexity of embedded indexed concept parsers

Weighted key words (with and without stemming)
Synonym sets
Abstraction relationships among index concepts
Partonomic relationships among index concepts
Target concepts serving as their own index concept
The first stage is key words. In a key word system, individual words (that is, the surface representations themselves, not the concepts behind them) form the universe of index concepts to associate to target concepts. In the experiments described previously, we used a morphological stemmer to group words related by the same root (such as {"eat" "eats" "eating," "ate" "eaten"} and {"dog" "dogs"}). Whether or not a stemmer is used, the main point is that the surface forms are used as indices.

The second stage is synonym sets. In this stage, key words are grouped according to their functional role within an application. For example, in the Casper tutor, {"bits" "particles" "pieces"} all perform the same role. In terms of the architecture of indexed concept parsing, this means creating an internal representation as the index concept (as in our example, m-water-bits) and assigning the members of the synonym set as phrasal patterns to that representation.

The third stage is the abstraction stage. In this stage, index concepts are assigned hierarchical relationships among themselves. For example, in the Casper tutor, the index concept m-smell was defined as a type of m-water-quality-problem. This allowed the Casper parser to recognize "How long have you had the smell?" as a way of saying "How long have you had the problem?" In terms of the architecture of indexed concept parsing, this means establishing abstraction relationships among index concepts. The parser in Casper was of this stage of complexity.

The fourth stage is the partonomic stage. In this stage, index concepts are assigned partonomic relationships among themselves. Although there was not a need for this in the Casper parser, imagine that there is a target concept:

ASK-COLOURED-BITS Do you have coloured bits in your water?

and that asking whether the customer's water contained black bits, brown bits, etc. should count the same as asking whether the water contained colored bits. In this case, an index concept, m-coloured-water-bits which had a :colour which was some m-colour, and an :object which is m-water-bits. With the conceptual relationships and phrasal patterns described and shown in Fig. 9.1, the index concept m-coloured-water-bits can be recognized in, for example:

Student: Do you have brown bits in your water?

from the phrase "brown bits."

If only abstraction relationships were allowed, as in Stage 3, one could associate the index concepts {m-colour m-water-bits} with this target concept, but then the necessarily more specific index concept m-coloured-water-bits (specific in the sense of having a higher information value) could not be defined. In the parser built for the Casper tutor, as we stated in Chapter 6, the use of structured index concepts was not needed[2].

The fifth stage is direct target concept recognition. In this stage, target concepts are matched by directly recognizing references to the target concept in the input text. In essence, this can be done using Direct Memory Access Parsing, as described in Chapters 2 and 3. To incorporate DMAP-style recognition of target concepts into the indexed concept parsing architecture requires minimal work beyond the knowledge representation required for any DMAP parsing. This is how it can be done: An index concept set is created for a target concept that contains exactly one member, which is the target concept itself. An example of a conceptual memory fragment to do direct target concept recognition is shown in Fig. 9.1. Because a target concept assigned only to itself necessarily has the highest possible information value, appraisers based on information value must return the target concept as the best match if the target concept is referenced in the input text[3].

What stage parser should one build?

An important distinction among the different stages of the unified architecture is the complexity and amount of knowledge representation done at the various stages. In the first stage, no knowledge representation is done; the surface forms of words of the text associated with target concepts are used as the index concepts, without human intervention. In the second stage, manual grouping of words form synonym sets, which are then assigned to index concepts[4]. A content specialist is required for Stages 3-5, to assign the relevant hierarchical and partonomic relationships and phrasal patterns.

Another hoped for distinction is the improvement in accuracy measures. In the experiments on the Casper data, significant differences exist between the index term systems and the Stage 3 indexed concept parsing system, with index concept parsing performing significantly better (with the improvement in perfect recall especially notable--from 44% to 75%). However, our experience in building a DMAP-style parser for Casper (see Chapter 4) cautions us that it may be very difficult to achieve these results, and that quite a bit of knowledge representation might be required to achieve marginal improvements.

Heuristically speaking, it might be best to start with a key word system (Stage 1). If the performance is acceptable, then all is well; otherwise, building a Stage 3 parser (indexed concept system with abstraction relationships) using tools such as those described in Chapter 8 is recommended. If there isn't the luxury of trying out the system on several iterations of user trials, then a Stage 3 parser may provide a good trade-off of effort for results. When there is a lot of contextual support for the parser, as in the Creanimate parser, then it may be possible to build a Stage 4 or Stage 5 parser.

The advantage of the unified architecture

Having a unified architecture for conceptual parsing provides a couple of advantages to the software engineer charged with creating an embedded conceptual parser. The first advantage is that it creates the possibility for a single parsing tool set that can be applied to a wide variety of parsing problems. The heuristics in the previous section, for example, suggest that a system developer might want to adjust the parsing technology when faced with inadequate accuracy or a need to lower development costs. By using a single tool set, the developer does not have to switch parsing technologies in mid-project, with concomitant costs in re-engineering. Using such a tool should make it easier to add parsing capabilities to new projects, as well, as standard tools for creating index concepts, associating them with target concepts, defining phrasal patterns, debugging content, evaluation tools, etc., are developed.

Another advantage to a unified architecture of the sort we have proposed is this: representations at the various stages can be mixed within the same parsing system. For example, one can imagine providing the knowledge representation required to match to some target concepts (a Stage 5 parser) with most target concepts being matched using indexed concepts in an abstraction hierarchy (a Stage 3 parser).

The main disadvantage of a unified architecture may be a degradation in performance, as specialized parsing and matching mechanisms are passed over in favor of a unified tool. This disadvantage can be overcome in one of two ways: either by carefully crafting and tuning the unified tool, or by using specialized mechanisms for special cases.

The unified architecture provides a blueprint for creating a unified tool for conceptual parsing, which would provide significant productivity improvements to the system designer thinking of embedding a conceptual parser in an application program.

As always, there is further work to be done. In fact, indexed concept parsing and the universal parsing architecture has been defined just to make parsing extensible to other natural language engineering tasks. The further work that needs to be done includes applying indexed concept parsing to new tasks and building a set of tools that instantiate the universal parsing architecture.

Indexed concept parsing and other natural language engineering tasks

This dissertation has described indexed concept parsing primarily for the task of interactive tutorial dialogs, but it is possible that indexed concept parsing (and associated ideas) can be used for other natural language engineering tasks, including other human-computer interaction tasks, text categorization and information extraction. These extensions are more or less difficult to make; we describe them from the easiest to the most difficult.

Indexed concept parsing and managing interactive dialogs

The first application of indexed concept parsing was the Casper customer service representative tutor, and this provides the prototype for useful applications of indexed concept parsing. As the parsing engine behind a human-computer interface in dialog systems, indexed concept parsing has shown its effectiveness in the Casper task. Other applications with similar specifications to Casper will also be improved by the addition of an indexed concept parser. The specifications include:

a model of interaction in which, at each point, the application is built to respond to a set number of user inputs,

the space of ways to express each of the user inputs is moderately limited,[5]

the use of free-form text would be advantageous,

choosing from the top n best results is acceptable.

However, with the unified parsing architecture we described previously in the chapter, an indexed concept parser would also be useful for applications such as Creanimate, even though the rich memory structure underlying Creanimate and the contextual supports all brought about the possibility of using Direct Memory Access Parsing. Because DMAP-based parsing can be incorporated into the parsing architecture, Creanimate-style applications can also be created using this architecture.

Indexed concept parsing and Ask systems

In Chapter 8, we described a prototype indexed concept parser in the TransAsk logistics advisor. This parser has two potential audiences:

Content specialists creating and editing the TransAsk system,

Logistics planners using the TransAsk system.

These two audiences have different needs, but a parser based on indexed concept parsing could be of benefit to both sets of users. For the content specialist, the parser can assist the work of indexing by providing a flexible means for navigating the Ask system, getting right to the question answered, question raised, or story being indexed. It can also potentially assist the content specialist in tracking down duplicate questions answered or questions raised, because the use of the parser provides a soft match between questions.

For the logistics planner, two uses of indexed concept parsing are envisioned: one is as a parsing zoomer. That is, a parser would provide an entrée into the entire Ask system, in which the planner gets an answer to a specific question which he or she brings to the system. The other use is as an aide to return to answers to previously asked questions; that is, as a way to get back to where the planner has been before.

These benefits of indexed concept parsing would accrue to any Ask system, both for the content specialists creating the Ask system as well as the clients for whom the Ask system was developed. Content specialists could use indexed concept parsing for quick navigation of the Ask system, and finding groups of questions that are on similar topics. Clients could use indexed concept parsing for a parsing zoomer or for returning to answers to previously asked questions.

Indexed concept parsing and information retrieval tasks

As we previously stated in this chapter, the architecture for information retrieval can be incorporated into the general indexed concept parsing architecture. This suggest that "information retrieval" systems might benefit from the architecture of indexed concept parsing, in particular, that the knowledge representation/accuracy trade-off might be worth investigating for individual information retrieval tasks.

It may be the case that techniques being developed within the information retrieval paradigm may prove beneficial in creating improved indexed concept parsers, ideas such as relevance feedback (Salton 1988), alternative inference mechanisms (Croft 1990), and latent semantic indexing (Dumais, Furnas, & Landauer 1990). In any case, it is hoped that the large divide that exists between statistically-based information retrieval and knowledge-intensive natural language processing has been partially bridged in this discussion of indexed concept parsing.

Indexed concept parsing, text categorization, and information extraction

The text categorization task is to assign texts to one of a small set of categories; for example, routing all news feed articles about mining to those with a need to know (Hayes 1990), or finding previous examples of solutions to problems coming into a computer help desk. Again, it may be possible to extend indexed concept parsing to these sorts of text categorization tasks. In particular, the unified parsing architecture suggests explicit structure in the text can be integrated with internal conceptual representations and more implicit structure in the text. Consider the following example, which might come from a data base of computer help desk problems:

Date: 6 June 1994

Machine: LJ HyperPrinter III

Problem: toner spills on paper

Long Desc: When the user came to get her output, there were splotches of toner all over the paper. She was really upset.

Part: Toner Cartridge

Solution: I swapped the toner cartridge out for a new one and it seemed to work after that.

Consider the following user query:

Machine: Laser Applewriter

Problem: paper has toner all over it

Assuming there is a conceptual memory that represented laser printers, individual models, problems, etc., we might imagine phrasal patterns that took advantage of the structured nature of the text, that is, the field names such as "Machine:". Indexed concept parsing was developed to allow robust parsing texts such as "toner spills on paper" or "paper has toner all over it" as the same internal representation of the problem[6]. So, the unified architecture provides a single method for integrating structured knowledge, structured text and unstructured text.

The task of information extraction is that of transforming incoming text into some structured internal representation, such as those systems involved in the Message Understanding Conferences (MUC 1991, 1992, 1993). Many of the same comments about text categorization apply here, as well. The unified architecture would allow for a method of robust parsing for information extraction.

The MUC conferences focussed on batch processing of large numbers of texts, but one could imagine useful information extraction systems that were interactive. For example, consider the task of converting existing texts (such as encyclopedia texts) into a format from which Ask systems could be built. In Fitzgerald and Wisdo (1994), we described how to use natural language processing to suggest connections among texts using Direct Memory Access Parsing. The key is that we imagine an interactive Story Editing Tool that a content specialist could use to review suggestions made by the natural language processing system and accept, reject or edit them as useful representations of the text.

Building the Embedded Conceptual Parser Tool Set

In addition to extending indexed concept parsing to other natural language engineering tasks, a useful next step would be to build a universal embedded conceptual parsing tool set based on the architecture described above, which would comprise three groups of tools: a parser definition tool, tools for knowledge engineering, and tools for debugging and evaluation.

The parser definition tool

In Section 9.3, we defined five stages of indexed concept parsers, based particularly on the complexity of the underlying knowledge representation required. A parser definition tool would allow a system designer to specify exactly which type of indexed concept parser to define. It should also contain advice about the advantages and disadvantages of the different stages of architecture, perhaps built on the experiences of previous designs. Part of defining such a tool would be to describe an application program interface (API) for the parser that can be used by the embedding application.

Tools for knowledge engineering

Chapter 8 describes several tools that were developed for the TransAsk logistics advisor parser. These knowledge engineering tools, built for the content specialist, were important for the creation of the TransAsk parser, and would be important for any attempt at architecting a parser. The TransAsk system had the following tool set:

A target concept browser (in the case of TransAsk, this was a list of questions answered),

An index concept set editor,

An index concept editor,

A phrasal pattern editor.

Tools for each of these tasks would be part of the knowledge engineering tool set. The target concept browser would allow a content specialist to choose a target concept to index. The index concept set editor would allow the specialist to associate sets of index concepts to target concepts. The index concept editor would allow the specialist to create, delete and modify new index concepts. The phrasal pattern editor would allow the specialist to associate phrasal patterns with index concepts.

Note that the stage at which one builds an indexed concept parser will affect which tools are needed, and how the tools work. A Stage 1 parser (key words) would only need a way to identify the target concepts and their associated text; the rest should occur automatically. A Stage 4 or 5 parser, on the other hand, would have to allow a specialist to define hierarchical and partonomic relationships between items in conceptual memory.

Note also that it is often the case that application programs come with their own editors. The parser definition tool should also allow the system developer to specify which tools will be needed, and define APIs for the other cases. Finally, note that the use of indexed concept parsing within the tools can provide useful functionality. In the TransAsk index concept set editor, the parser was used to suggest index concept sets based on the text of questions answered, and in the TransAsk index concept editor, the parser was used to locate index concepts within the conceptual memory. This functionality should be carried over into the knowledge engineering tools.

Tools for debugging and evaluation

Finally, the tool set should contain software tools for debugging and evaluation, tools which can be run interactively, or in batch mode. A foundation for debugging and evaluation is a standard method of logging the use of the parser, and its results[7]. Such a logging facility would have advantages beyond simply improving the parser: the facility could be used by the embedding application program to log other events for further analysis. Again, a standard format for logging, a logging definition language, would be of use for evaluation and debugging. This language would define such standard features as sessions and session identifiers, and would allow a system designer to define simple events (such as button pushes) and structured events (such as a call to the parser and the returned results, or the initiation of watching a video, tracking how many times the video was seen, etc.). Depending on which stage parser is defined with the parser definition tool, standard logging language code could be created.

Using this standard logging facility would allow the creation of a standard interactive tool for testing parses, such as the tools built for the Creanimate parser (Chapter 3) and the TransAsk parser (Chapter 8), which would provide explanations for the results of parsing at the appropriate level--for example, the interactive tool should be able to present the results of the various appraisers to the content specialist so he or she understands why a particular result was returned.

Using the logging facility would also allow comparison and regression testing of a parser as changes are made in representation or even stage. We imagine another tool, called the standard evaluation tool, that would convert the logs created by the logging facility into a format that would allow comparison between different parsers or versions of the same parser (as we have done throughout this work). Such a tool would allow a system designer or content specialist to tag the results of individual parses with correct parses (thereby creating the oracle needed to measure accuracy), as well as other salient features, such as the state of the program (so that expectation sets can be defined, for example, or possible pronominal disambiguation). The standard evaluation tool would then allow different parsers to be run on the same set of data, so comparisons can be made.

The Embedded Conceptual Parser Tool Set: A summary

The creation of an embedded parsing tool set would comprise three sets of interrelated tools. The parser definition tool would allow a system designer to set the general parameters of the parser, and effect the operation of the other tools. The knowledge engineering tools would allow a content specialist to create the conceptual representations required to make the parser work within a program. The evaluation and debugging tools would allow the system designer and content specialist to evaluate and then modify the parser and the underlying knowledge representations.

In addition to these software tools, application program interfaces should be defined, including APIs for communication between the embedding application and the embedded parser; between possibly preexisting knowledge engineering tools and the parser; and for standard logging. Building this tool set will provide a useful way to add conceptual parsers to application programs.


Building conceptual parsers for Creanimate, Casper and TransAsk has provided a means for discovering new ways to think about parsing. Because these parsers were built for accomplishing real-world tasks, we have had to focus on, and in some cases develop, ways of evaluating the parsers we build--measurements of how well the parsers have done their task. It has also led the way to a new architecture for parsing, indexed concept parsing, that proved to be an effective way of allowing users of the Casper tutor to say what they wanted to say in a relatively open ended way. The development of the technique of indexed concept parsing led us to suspect that a continuum exists that describes a space of parsers, that moves from simple key word systems on the one end to structured conceptual parsing on the other. We have described this continuum above, and suggested ways in which a universal embedded conceptual parser tool set could be developed.

[1.]KR: Knowledge representation

[2.]The predicted and unpredicted appraisers defined for indexed concept parsing using partonomic relationships need to be written to take into account the presence of index concepts which are part of other index concepts (see Chapters 5 and 7).

[3.]Although an appraiser based on expectations in context (as described in Chapter 6) might override appraisers based on information value. Depending on the needs of the application, this may be exactly what is needed. The appraisers based on information value will need to be sensitive to partonomic relationships, as in Stage 4.

[4.]Statistical techniques exist to create synonym sets, as well, such as latent semantic indexing (Dumais, Furnas, & Landauer 1990), which may obviate the need to manually assign synonyms. A previously defined thesaurus might be used as well.

[5.]As an example of a space that is not limited, consider a system in which the system knows how to respond to making small talk, but the number of ways that a user might express that small talk is very large. See, for example, the Guss application, Yello (Burke 1993).

[6.]One might imagine this occurring in an interactive dialog, in which the user of the system enters "paper has toner all over it" and the parser returns a canonical form for toner spills.

[7.]A simple logging facility is defined in the appendix.