Concretion: assumption-based understanding

A language understanding program must produce as precise a meaning representation as possible from a linguistic input. CONCRETION is the process of developing a specific interpretation by combining various levels of conceptual information. This process represents an assumption-based method of language interpretation, and departs from the traditional approach of treating multiple interpretations as independent. Concretion Mlows the language analyzer to develop a sufficiently specific representation without excessive computation or brittle interpretation rules. 1 I n t r o d u c t i o n The ambiguity and imprecision of language are the key problems in building language understanding programs. Most systems tha t perform semantic interpretation [Bobrow and Webber, 1980, Sondheimer et al., 1984, Lytinen, 1984, Hirst, 1987] address this imprecision by offering means of selecting among alternative interpretations. The problem with these approaches is tha t they fail to take into account the interrelationshiPS among the interpretations, which often support or refute one another to various degrees. A bet ter model is one in which the candidates exist not as distinct choices but as assumptions contributing to a complete meaning representation. The language understanding process thus gradually refines a semantic representation based on the support or refutation of each element. For example, consider the following potential inputs: 1. John cut the salami. 2. John gave a kiss to Mary. 3. The investor group seeking control of Warnaco... 4. The ~rm' command takes three arguments. 5. Move back to the last position. The examples above represent potential inputs from several disparate real and "toy" domains of TRUMP (TRansportable Understanding Mechanism Package)[Jacobs, 1986b, Jacobs, 1987]. The idea of TRUMP is to use a combination of "core" knowledge about language and certain specialized knowledge to produce a complete semantic interpret~ation. In each of the examples, the italicized word or phrase represents a vague, ambiguous, or metaphorical verb sense. The problem for a good semantic interpreter is to derive the real o r intended sense of each phrase without excessive computation or specialized knowledge. For example, the following are reasonable paraphrases of a complete semantic interpretation of the above examples: 1. John cut the salami cut =~ sliced (NOT chopped or shortened) 27oi 2. John gave a kiss to Mary gave a kiss ~ kissed (NOT presented) 3. The investor group seeking control of Warnaco... seeking =~ trying (NOT searching) 4. The ~rm' command takes three argmnents. takes ~ requires as input (NOT moves) 5. Move back to the last position. Move back =# return (NOT move backwards) Each of these examples represents a clear, ordinary use of language. Yet a semantic interpreter must use a great deal of knowledge to distinguish the intended sense of the italicized phrase from other related ahd competing senses. It is simply not practical to treat this process as one of discriminating among a large set of distinct interpretations. The space of intended meanings is too large, and there are too many common characteristics of various senses. To deal effectively with the complexity of this process, a semantic interpreter must accomplish the following: 1. Identify prospective in terpre ta t ions-The system must use linguistic information to select interpretations tha t are consistent with the input. 2. Use linguistic and conceptual knowledge to combine interpretat ions-This may result in ruling out certain candidates, or in forming new and more precise interpretations from the combination of knowledge sources. 3. Assume a specific in terpre ta t ion--As in the above examples, a practical understanding of the input must be somewhat more than the maximum that can be "safely" inferred. The system must produce some knowledge structures tha t are likely candidates but are not certain from the linguistic description. 4. Fail gracefully on contradict ions--If an assumed interpreta t ion results in a contradiction, the system must preserve those interpretations tha t do not conflict. If other interpretations are dependent on a conflicting one, these too must be discarded. The requirements above suggest a model of language understanding tha t progressively refines a semantic intet~preta tion, based on linguistic and contextual information, but tha t incorporates into each specific interpretation knowledge upon which tha t interpretat ion builds. In other words, the ult imate goal of the system is to produce the most specific consistent interpretation, and the means of achieving that goal is to treat each interpretat ion as an assumption. This assumption-based interpretation process is known as CONCRETION * [Wilensky, 1983]. The idea of concretion is to *This term was originally proposed by Joe Faletti. The problem of concretion was initially defined in a series of seminars conducted by Robert determine ~u~ specific a meaning as is possible from an input, while enabliltg recovery if this interpretation proves overly specific. This process is the essential element of a framework that satisfies the criteria mentioned above. Concretion is an important method for dealing with the problem of vagueness and imprecision as framed above. A system tha t pexforms concretion can successfully produce a complete interpretat ion without overcommitting to such an interpretation. The discussion that follows describes the concretion process as implemented in TRUMP and considers how this technique improves upon previous approaches. 2 C o n c r e t i o n Concretion :b~ the process of taking abstract concepts and prodat ing from ~hem concepts tha t are more precise, or concrete. The motivation for this mechanism is strong in story understanding [Norvig, 1983, Wilensky, 1983], because understanding a story ~,eems to involve a continuous refinement of the major concepts into more specific categories. Concretion does not really involve inferencc, since often the specific meaning is quite explicit, in the text. The process of concretion is evident in understmlding simple words and phrases in limited linguistic contexts as well, as illustrated in the examples presented earlier. Concretion is important because it is the mechanism that allows general knowledge about language to apply at very specific levels o{ semantic interpretation. This is essential for natural language interfaces and well as text processing systems, because it allows a core of linguistic and conceptual knowledge to be used for a variety of domains, mid makes the addition of domain-specific linguistic knowledge easier. For example, knowledge about verbs such as give and take and their relation to transfer-events applies in discussing operating systems or corporate ~akeovers as well as in more general applications. It is hard to see how portability can be achieved without thc capability to entertain a range of inter,elated meaning representations. A typical natural language input can test several aspects of the concretion process. In example 3, the investor group seeking control of Warnaco, A first-pass semantic analysis derives a seeking ac t ion-The investor group is the searcher, and control of Warnaco is being sought. Domain-independent conceptual knowledge suggests tha t looking for a state means trying to realize the state. Domain-specific knowledge produces the assmnpti(m that the phrase describes a corporate takeover at tempt. An interpretat ion of this specificity is necessary to drive inferences and cooperative responses. The concretion process is illustrated in figure 1. Each stage descril)ed above, and each knowledge source, must be distinct. The surface level semmltic analysis is essential because it derives conceptual relations that would apply also to "looking for", "searching for", and even "pursuing"; this analysis thus avoids the redundant representation of each construct. This intermediate analysis also makes it possible to use abstract conceptual roles (such as actor and recipient) to determine specific underlying roles (such as the target and suitor of a corporate takeover) (of. [Jacobs, 1987]). The second aspect of concretion, applying conceptual knowledge to produce Wilensky at the University of California at Berkeley. In addition to Wilensky, Faletti, and the author, participants in these seminars included Yigal Arens, Margare~ Butler, David Chin, Chuck Fillmore,' Paul Kay, Marc Luria, Jim Martin, Jim Mayfield, Peter Norvig, Lisa Rau, and Nigel Ward. " T h e investor group seeking control of Warnaco"