Contextual vocabulary acquisition (CVA) is the active, deliberate acquisition of a meaning for an unknown word in a text by reasoning from textual clues, prior knowledge, and hypotheses developed from prior encounters with the word, but without external sources of help such as dictionaries or people. Published strategies for CVA vaguely and unhelpfully tell the reader to ‘guess’. Artificial intelligence algorithms for CVA can fill in the details that replace ‘guessing’ by ‘computing’; these details can then be converted to a curriculum that can be taught to students to improve their reading comprehension. Such algorithms also suggest a way out of the Chinese Room and show how holistic semantics can withstand certain objections.

This eagerly awaited anthology, while surely not the last word on the Turing Test, equally surely deserves to become the principal source of information on the test.It includes not only Turing's classic paper, but a fine selection of the main replies to date, all tied together by an engaging and penetrating essay by the editor.Stuart M. Shieber's name is well known to computational linguists for his research and to computer scientists more generally for his debate on the Loebner Turing Test competition, which appeared a decade earlier in Communications of the ACM (Shieber 1994a(Shieber , 1994b;;Loebner 1994). 1 With this collection, I expect it to become equally well known to philosophers.The collection begins with historical "precursors" to Turing's paper: two pieces by Descartes-his Discourse on the Method, Chap.V (1637), and his "Letter to the Marquess of Newcastle"-followed by selections from La Mettrie's Machine Man (1748).The second part contains the centerpiece: Turing's 1950 paper from Mind, "Computing Machinery and Intelligence," accompanied by three "ephemera": two early (1951) and difficult-to-find articles by Turing-"Intelligent Machinery, a Heretical Theory" and "Can Digital Computers Think?"-and a transcript of a 1952 BBC radio interview with Turing, M. H. A. Newman, Sir Geoffrey Jefferson, and R. B. Braithwaite, "Can automatic Calculating Machines Be Said to Think?" Shieber's presentation of the pièce de résistance (Turing 1950) devotes great attention to the sanctity of the text and is replete with scholarly paraphernalia comparing his carefully edited reprint with the original (which, by the way, is now available online, courtesy of JSTOR.org).The third, and final, part contains the immediate reactions to Turing's Mind paper as they appeared in that journal, followed by now-classic responses and some more-recent, important papers, some arranged chronologically, others logically.The first published response was Leonard Pinsky's early (1951)-and satirical-"Do Machines Think about Machines Thinking?" for which Shieber offers a brief, wry introduction.Next we have a quartet consisting of Keith Gunderson's important "The Imitation Game" (1964), Richard Purtill's response ("Beating the Imitation Game," 1971), and Geoffrey Sampson's ("In Defence of Turing") and P. H. Millar's ("On the Point of the Imitation Game") 1973 replies to Purtill.Jumping ahead a couple of decades comes Robert M. French's 1990 "Subcognition and the Limits of the Turing Test."Next, in more of a logical than a chronological order, comes a trio consisting of John 1 Interestingly, neither Loebner nor his competition are mentioned by name, or even discussed, in the book under review, though there is a citation to Shieber 1994a.

This essay explores the implications of the thesis that implementation is semantic interpretation. Implementation is (at least) a ternary relation: I is an implementation of an ‘Abstraction’ A in some medium M. Examples are presented from the arts, from language, from computer science and from cognitive science, where both brains and computers can be understood as implementing a ‘mind Abstraction’. Implementations have side effects due to the implementing medium; these can account for several puzzles surrounding qualia. Finally, an argument for benign panpsychism is developed.

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test-passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably be persons.

Narrative passages told from a character's perspective convey the character's thoughts and perceptions. We present a discourse process that recognizes characters' thoughts and perceptions in third-person narrative. An effect of perspective on reference in narrative is addressed: references in passages told from the perspective of a character reflect the character's beliefs. An algorithm that uses the results of our discourse process to understand references with respect to an appropriate set of beliefs is presented.

Belief reports can be interpreted de re or de dicto, and we investigate the disambiguation of belief reports as they appear in discourse and narrative. In earlier work, representations for de re and de dicto belief reports were presented, and the distinction between de re and de dicto belief reports was made solely on the basis of their representations. This analysis is sufficient only when belief reports are considered in isolation. We need to consider more complicated belief structures, in addition to those presented earlier, in order to sufficiently represent de re and de dicto belief reports as they appear in discourse and narrative. Further, we cannot meaningfully apply one, but not the other, of the concepts de re and de dicto to these more complicated belief structures. We argue that the concepts de re and de dicto do not apply to an agent's conceptual representation of her beliefs, but that they apply to the utterance of a belief report on a specific occasion. A cognitive agent interprets a belief report such as "S believes that N is F," or "S said, 'N is F'" (where S and N are names or descriptions, and F is an adjective) de dicto if she interprets it from N's perspective, and she interprets it de re if she interprets it from her own perspective.

Tilts paper surveys several approaches to semanttc-netw,~rk semantics that have not previously been treated ~n the AI or computattonal lingutsttcs hterature, though there ~s a large ptulu.~)ph~cal hterature invest)gating them m ~mledetad.In parttcular, proF~n~onal semanttc networks (exemphhed hv ~,NeP%)are dis cus.~d, it ts argued that ~mlv a Iull'; mtenstonal ("Mem(mgtan") semantics is apprt)prtate I(~r them.and se'~eral \|eln(~nglan svstenls are presented.

We discuss how a deductive question-answering system can represent the beliefs or other cognitive states of users, of other (interacting) systems, and of itself. In particular, we examine the representation of first-person beliefs of others (e.g., the system's representation of a user's belief that he himself is rich). Such beliefs have as an essential component "quasi-indexical pronouns" (e.g., 'he himself'), and, hence, require for their analysis a method of representing these pronominal constructions and performing valid inferences with them. The theoretical justification for the approach to be discussed is the representation of nested "de dicto" beliefs (e.g., the system's belief that user-1 believes that system-2 believes that user-2 is rich). We discuss a computer implementation of these representations using the Semantic Network Processing System (SNePS) and an ATN parser-generator with a question-answering capability.