Data-Oriented Language Processing. An Overview

During the last few years, a new approach to language processing has started to emerge, which has become known under various labels such as "data-oriented parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak 1996a/b; Goodman 1996; Kaplan 1996; Rajman 1995a/b; Scha 1990-92; Sekine & Grishman 1995; Sima'an et al. 1994; Sima'an 1995-96; Tugwell 1995). This approach, which we will call "data-oriented processing" or "DOP", embodies the assumption that human language perception and production works with representations of concrete past language experiences, rather than with abstract linguistic rules. The models that instantiate this approach therefore maintain large corpora of linguistic representations of previously occurring utterances. When processing a new input utterance, analyses of this utterance are constructed by combining fragments from the corpus; the occurrence-frequencies of the fragments are used to estimate which analysis is the most probable one. In this paper we give an in-depth discussion of a data-oriented processing model which employs a corpus of labelled phrase-structure trees. Then we review some other models that instantiate the DOP approach. Many of these models also employ labelled phrase-structure trees, but use different criteria for extracting fragments from the corpus or employ different disambiguation strategies (Bod 1996b; Charniak 1996a/b; Goodman 1996; Rajman 1995a/b; Sekine & Grishman 1995; Sima'an 1995-96); other models use richer formalisms for their corpus annotations (van den Berg et al. 1994; Bod et al., 1996a/b; Bonnema 1996; Kaplan 1996; Tugwell 1995).Comment: 34 pages, Postscrip

Parallel Distributed Grammar Engineering for Practical Applications

Based on a detailed case study of parallel grammar development distributed across two sites, we review some of the requirements for regression testing in grammar engineering, summarize our approach to systematic competence and performance profiling, and discuss our experience with grammar development for a commercial application. If possible, the workshop presentation will be organized around a software demonstration

Knowledge Representation, Heuristics, and Awareness in Artificial Grammar Learning

People can become sensitive to the general structure of different parts of the environment, often without studying that general structure directly, but through being incidentally exposed to instances that conform to the structure. When such learning proceeds unintentionally and gives rise to knowledge that is difficult to verbalize it is often referred to as implicit learning. One of the most commonly used experimental paradigms in the study of implicit learning is artificial grammar learning, in which participants are exposed to sequences that conform to a set of rules without being informed about the presence of rules. In a subsequent test phase, participants can usually distinguish between sequences that conform to and sequences that violate the rules, without being able to say much about the underlying rules. There are many different theories about the kind of knowledge representations that underlie sensitivity to general structure in artificial grammar learning, and there are also different viewpoints concerning how to measure the conscious status of the knowledge acquired in artificial grammar learning. Investigating these different theories is important, partly because it may provide an understanding of the extent to which complex learning and abstraction of structure proceeds unconsciously. Study I of this thesis investigated artificial grammar learning and the use of a fluency heuristic, which involves relying on the surprising ease of processing an item as a basis for making a judgment. Other studies have shown that the fluency heuristic is used in a wide variety of judgments (e.g., recognition and preference). Study I showed that participants rely on a fluency heuristic in artificial grammar learning as well, but mainly under non-analytic pro¬ces¬sing conditions when participants were encouraged to respond rapidly and thereby make global judgments about items without processing details to any large extent. This is consistent with the idea that fluency may provide a cue for indirect sensitivity to general structure. Study II investigated the effect of non-analytic processing on the conscious status of knowledge as assessed by confidence judgments. It was found that non-analytic processing increased the availability of conscious knowledge, consistent with the idea that part of the knowledge acquired in artificial grammar learning may be, not inherently unconscious, but of a kind that is available through a non-analytic form of introspection. One possibility is that, relative to more analytic forms of introspection, non-analytic introspection may be more sensitive to the non-focal peripheral contents of consciousness, the so called “fringe consciousness”. This could explain why the knowledge acquired in artificial grammar learning often seems intuitive, even though it is not necessarily unconscious. Study III investigated whether artificial grammar learning gives rise to knowledge that is independent from the surface features of the exposure material. A number of claims have been offered in the literature for such surface-independent knowledge, particularly as a result of extended exposure to regularities. The results clearly suggested that the knowledge formed under observational learning conditions in artificial grammar learning is not independent from the surface features of the exposure material. The results are consistent with a variety of computational models of artificial grammar learning that rely on surface-dependent perceptual representations. Finally, Study IV investigated whether the knowledge acquired in artificial grammar learning is unconscious in the sense that it may be expressed unintentionally. The results showed that, to the extent that knowledge was expressed, it was expressed intentionally. However, the low levels of performance in Study IV limit the generality of the findings. Possible reasons for the low performance are discussed in the context of different models of artificial grammar learning. Taken together, the studies in this thesis illuminate issues regarding both knowledge representation and the conscious status of knowledge in artificial grammar learning. In general, the studies are in line with an episodic framework according to which the general abstract structure of a domain is not automatically extracted. Instead, both learning and awareness proceeds as a function of task demands, intentions, expectations, and processing strategies

Deduction over Mixed-Level Logic Representations for Text Passage Retrieval

A system is described that uses a mixed-level representation of (part of) meaning of natural language documents (based on standard Horn Clause Logic) and a variable-depth search strategy that distinguishes between the different levels of abstraction in the knowledge representation to locate specific passages in the documents. Mixed-level representations as well as variable-depth search strategies are applicable in fields outside that of NLP.Comment: 8 pages, Proceedings of the Eighth International Conference on Tools with Artificial Intelligence (TAI'96), Los Alamitos C

Theories about architecture and performance of multi-agent systems

Multi-agent systems are promising as models of organization because they are based on the idea that most work in human organizations is done based on intelligence, communication, cooperation, and massive parallel processing. They offer an alternative for system theories of organization, which are rather abstract of nature and do not pay attention to the agent level. In contrast, classical organization theories offer a rather rich source of inspiration for developing multi-agent models because of their focus on the agent level. This paper studies the plausibility of theoretical choices in the construction of multi-agent systems. Multi-agent systems have to be plausible from a philosophical, psychological, and organizational point of view. For each of these points of view, alternative theories exist. Philosophically, the organization can be seen from the viewpoints of realism and constructivism. Psychologically, several agent types can be distinguished. A main problem in the construction of psychologically plausible computer agents is the integration of response function systems with representational systems. Organizationally, we study aspects of the architecture of multi-agent systems, namely topology, system function decomposition, coordination and synchronization of agent processes, and distribution of knowledge and language characteristics among agents. For each of these aspects, several theoretical perspectives exist.