Publications

Abstract

Students who are learning to write in a foreign language, often want feedback on the grammatical quality of the sentences they produce. The usual NLP approach to this problem is based on parsing student-generated text. Here, we propose a generation-based ap- proach aiming at preventing errors ("scaffolding"). In our ICALL system, the student constructs sentences by composing syntactic trees out of lexically anchored "treelets" via a graphical drag & drop user interface. A natural-language generator computes all possible grammatically well-formed sentences entailed by the student-composed tree. It provides positive feedback if the student-composed tree belongs to the well-formed set, and negative feedback otherwise. If so requested by the student, it can substantiate the positive or negative feedback based on a comparison between the student-composed tree and its own trees (informative feedback on demand). In case of negative feedback, the system refuses to build the structure attempted by the student. Frequently occurring errors are handled in terms of "malrules." The system we describe is a prototype (implemented in JAVA and C++) which can be parameterized with respect to L1 and L2, the size of the lexicon, and the level of detail of the visually presented grammatical structures.

Abstract

We outline an algorithm capable of generating varied but natural sounding sequences of argument NPs in subordinate clauses of German, a semi-free word order language. In order to attain the right level of output flexibility, the algorithm considers (1) the relevant lexical properties of the head verb (not only transitivity type but also reflexivity, thematic relations expressed by the NPs, etc.), and (2) the animacy and definiteness values of the arguments, and their length. The relevant statistical data were extracted from the NEGRA–II treebank and from hand-coded features for animacy and definiteness. The algorithm maps the relevant properties onto “primary” versus “secondary” placement options in the generator. The algorithm is restricted in that it does not take into account linear order determinants related to the sentence’s information structure and its discourse context (e.g. contrastiveness). These factors may modulate the above preferences or license “tertiary” linear orders beyond the primary and secondary options considered here.

Abstract

A series of experiments is reported in which subjects describe simple visual scenes by means of both sentential and non-sentential responses. The data support the following statements about the lexicalization (word finding) process. (1) Words used by speakers in overt naming or sentence production responses are selected by a sequence of two lexical retrieval processes, the first yielding abstract pre-phonological items (Ll -items), the second one adding their phonological shapes (L2-items). (2) The selection of several Ll-items for a multi-word utterance can take place simultaneously. (3) A monitoring process is watching the output of Ll-lexicalization to check if it is in keeping with prevailing constraints upon utterance format. (4) Retrieval of the L2-item which corresponds with a given LI-item waits until the Ld-item has been checked by the monitor, and all other Ll-items needed for the utterance under construction have become available. A coherent picture of the lexicalization process begins to emerge when these characteristics are brought together with other empirical results in the area of naming and sentence production, e.g., picture naming reaction times (Seymour, 1979), speech errors (Garrett, 1980), and word order preferences (Bock, 1982).