Understanding Language Understanding: Computational Models of Reading edited by Ashwin Ram and Kenneth Moorman

MIT Press/Bradford Books, 1999. $50.00 (xii + 499 pages)ISBN 0 262 18192 4Understanding Language Understanding is a collection of 12 chapters by 21 authors at the core of language-comprehension research. Nearly half of the authors are psychologists, with the balance being computer scientists, an interdisciplinary mixture that I found to be a positive feature; instead of simply presenting results, the authors attempt to relate and evaluate their models in the context of psychological data.Any collection of articles in a given subfield is bound to be a hit-and-miss affair. I found about a third of the articles to be uninteresting (mainly because the coverage was either too shallow, too vague or discussed obvious results).The third chapter (Mahesh, Eiselt and Holbrook) contains an introduction to syntactic and semantic parsing at the sentence level. The authors give a good overview of one particular family of grammatical parsing strategies, namely, ‘left-corner’ strategies, in which parse trees are built up using both bottom-up and top-down information that is anchored at the leftmost ‘child’ of a given phrase structure. The authors go on to describe an improved variant of left-corner parsing, which they have implemented in a computer model called COMPERE.Two later chapters (5 and 6) describe connectionist models, in which all knowledge is represented by a network of connections between nodes and all processing tasks (such as inference, memory retrieval, and disambiguation) are achieved via spread of activation across the nodes in the network. Chapter 5 (Lange and Wharton) contains a very detailed and in-depth description of REMIND, a model that performs sophisticated word-sense disambiguation (in which the same word is reinterpreted multiple times as the context changes), inference of goals/plans, and reminding of analogous episodes, all by means of the same, general, spreading activation mechanism.Both connectionist models presented here are what are termed ‘localist’ or ‘structured’ connectionist models, in which each node represents a complete semantic or syntactic entity. For instance, activation on a single node might represent evidence for the concept DOG or the event EAT. In contrast, ‘distributed’ connectionist models represent each model-level entity as a pattern of activation over an entire ensemble of nodes. It is unfortunate that there is no chapter in Understanding Language Understanding that discusses distributed connectionist approaches to NLP (for an example of this approach, see Ref. 1xSubsymbolic Natural Language Processing. Miikkulainen, R. See all ReferencesRef. 1).Chapters 8, 10 and 11 constitute the core of the book. These chapters essentially describe the doctoral work of Ram and his students. The chapter by Ram himself (entitled ‘A Theory of Questions and Question Asking’) describes AQUA, a model that focuses on anomalous parts of text. An example story presented is a tale of a 16-year-old Lebanese boy called Mohammed, who is captured in a raid by Israeli troops before he is going to blow himself up in a suicide car-bomb mission. The story goes on to state that Mohammed was not a religious fanatic but rather that he was blackmailed into carrying out the suicide mission. This type of text is anomalous because the standard explanation for carrying out the action (i.e. religious fanaticism) is missing.Ram argues that the readers generate questions during comprehension that help them to focus processing and reduce a potentially combinatorial explosion of irrelevant inferences. For the car bomb story, such questions might include: Why are kids chosen for such missions? What fate worse than death did Lebanese terrorists threaten him with? How did the Israelis know where to carry out their raid?Many of the questions already exist in the reader’s mind before he begins reading. These prior questions come about as the result of earlier reading experiences, in which other anomalous events were encountered, but are not yet resolved. AQUA learns about the domain of terrorism by reading newspaper terrorism stories containing unusual events. To develop his model, Ram categorized questions into various types: (1) standard questions involve roles (e.g. ‘Was the car the instrument or the target of the bombing?’); (2) memory questions arise as similarities or coincidences are noticed (e.g. ‘Why are all Lebanese bombers named Mohammed?’); (3) explanation questions explore the goals of characters (e.g. ‘Why are teenagers chosen by terrorists to go on suicide missions?’); (4) relevance questions involve how the information is personally relevant to the reader (e.g. ‘How does a Lebanese teenager compare with a typical American teenager?’).AQUA operates in a similar way to many conceptually-based NLP systems; namely, it instantiates conceptual frames as phrases are encountered, and then generates expectations to fill in roles in these frames (and to connect frames via causal links; to infer frames, etc.). However, AQUA extends this process by focusing on recognizing and resolving anomalous situations.The AQUA approach is extended still further in chapter 10 (Moorman and Ram), in which Moorman describes his system, ISAAC. This system learns new concepts as the result of reading science fiction stories; for example, ISAAC reads a story in which the characters use knowledge of lycanthropy (the supposed transformation of a human being into a werewolf) to create a novel object – a ‘were-car’. A further AQUA-related system, Meta-AQUA, is described in the following chapter (by Cox and Ram), which also uses anomalies encountered during comprehension to learn new world knowledge.The final chapter (Riloff) gives a reasonable overview of the field of Information Extraction (IE), in which predefined types of information are extracted from natural language text. Most IE systems use phrasal patterns to extract frames and their roles. For instance, the phrasal pattern ‘was kidnapped by ’ can be used to build a KIDNAP frame with Y bound to the KIDNAPPER role. Riloff originally developed AutoSlog, a system that learned such phrasal patterns from a training set consisting of text in which words were already tagged by their appropriate roles. Here, Riloff describes AutoSlog-TS, an extension of AutoSlog that is able to learn frame-role phrasal patterns when trained on untagged text. The only information AutoSlog-TS is given is whether or not the piece of text it is reading is relevant to a particular conceptual frame. AutoSlog-TS uses a pre-existing parser to generate phrasal patterns and then gathers statistics (based on the relevant/irrelevant classification of pieces of text) in order to determine which phrasal patterns are most relevant for extracting the desired domain-related frames and roles.Two general complaints about Understanding Language Understanding might be, first, that the descriptions of the AQUA-related systems are still too general to be able to evaluate the amount of effort that would be needed to scale them up to human-level performance. And, second, that there has been a revolution in NLP as the result of the application of statistical methods (especially in the area of machine translation), and yet only one article in this collection deals with these empirical methods. Overall, though, I found this book to be a worthwhile addition to the literature on computational models of text comprehension.