Use Of Linguistic Information Research Articles

Neuro-fuzzy modeling and control of a batch process involving simultaneous reaction and distillation

This paper is concerned with a novel approach to batch process automation using fuzzy modeling and reinforcement learning. The core part of the automation strategy is an autonomous agent that continuously learns to implement control actions that can drive the batch process state very close to the desired one with near-optimal performance. An efficient algorithm for reinforcement learning called fuzzy Q-Learning is proposed to build the agent (controller). The use of linguistic information to guide the learning process and to implement near-optimal actions provides the means for both knowledge integration and scaling reinforcement learning. The methodology is exemplified using a batch process involving simultaneous reaction and distillation.

Integration of lexical and syntactical knowledge in a handwriting-recognition system

This article presents a research project carried out with the aim of investigating the improvements in recognition performances that result from the use of linguistic information in a handwriting-recognition system. The purpose of the study was to design a postprocessor that would enhance an existing handwriting-recognition system by identifying and correcting words it did not recognize initially. This was done by integrating linguistic information (both lexical and syntactical) into the system. Every sentence containing one or more incorrect words is parsed and all possible grammatical classes for each incorrect word are listed. Then, a lexical enquiry searches for words in the lexicon corresponding to the grammatical class of the word in question. Finally, a string-comparison algorithm selects only the words in the lexicon that are close to the incorrect word. The results of this experimentation show that such a system is more efficient in correcting words (even highly distorted ones) than conventional systems that only integrate lexical information. In conclusion, the integration of linguistic information to correct words not recognized by a handwritingrecognition system is shown to be an effective approach, and one that might be worth pursuing.

An investigation of late closure: The role of syntax, thematic structure, and pragmatics in initial interpretation.

Four reading-time experiments investigated the application of the late closure principle in Italian. The experiments tested the principle governing the initial attachment of different types of modifiers (relative clause, adjectival phrase, and prepositional phrase) to a complex noun phrase. By manipulating the type of preposition within the complex noun phrase, the authors investigated the role of the thematic structure in initial and final parsing. The results show that the late closure principle applies to initial parsing in Italian without being affected by the thematic structure of the complex noun phrase. Final interpretation, however, shows an effect of pragmatic preference and an effect of thematic structure on syntactic revisions. The results are discussed in terms of a parsing model that adopts syntactic parsing strategies and makes modular use of linguistic information. The purpose of this research was to assess whether late closure, an assumed universal sentence parsing principle (Frazier, 1978), applies in Italian. In this article we study the attachment of different types of modifiers to complex noun phrases, drawing a distinction between initial and final interpretation and trying to identify what variables (syntactic, semantic, and pragmatic) affect what stage in the comprehension process. The results of four on-line experiments conducted in Italian are presented. Kimball (1973) and Frazier and Fodor (1978) proposed strategies that apply to the initial parsing of a sentence as soon as each word is perceived. Some examples of such strategies are right association (Kimball, 1973), minimal attachment and late closure (Frazier & Fodor, 1978), superstrategy (Fodor, 1979), recent filler strategy (Frazier, Clifton, & Randall, 1983), active filler strategy (Frazier, 1987), and the minimal chain principle (De Vincenzi, 1991). The basic idea in all of these strategies is that they are directly derived from a simple principle: Choose to do whatever costs the least effort in terms of computation to interpret the incoming linguistic input before it decays. This choice is motivated by a basic cognitive reason, namely, the restrictions on short-term memory in terms of memory and computational space and the fact that

Some observations on the universality of the late-closure strategy

Two questionnaire studies and a reading time experiment investigated the application of the late-closure principle in Italian, a Romance language which contrasts with English with respect to several linguistic properties. All the studies addressed interpretation preferences in sentences containing a complex NP followed by a relative clause (e.g....the son of the woman who arrived yesterday...). While the questionnaires investigated final preferences, the reading time experiment addressed also the principle governing the initial attachment of a relative clause to a complex NP. Furthermore, through a manipulation of the type of preposition within the complex NP, we investigated the role of the thematic structure of the complex NP in initial and final parsing. The results showed that the late-closure principle applies in Italian to the initial parsing without being affected by the thematic structure of the complex NP. Final interpretation instead shows an effect of pragmatic preference and an effect of thematic structure on syntactic revisions. The results are discussed in terms of a parsing model that adopts syntactic parsing strategies and makes modular use of linguistic information. Some implications for the relationship between syntactic theories and the human parser are also addressed.

Language processing in connection with speech translation at ATR interpreting telephony research laboratories

The relationship between speech and language processing is an important problem to be solved in order to achieve continuous large-vocabulary speech recognition for a speech translation system or the human interface of a man-machine system. For the recognition of large-vocabulary continuous speech, first the phonemes are recognized by HMM (Hidden Markov Model). A generalized LR parser is introduced to predict next words/phonemes. The Japanese utterance is successfully recognized by the combined HMM-LR parser (HMM-LR). Many phrase candidates are filtered out of the speech recognition system through the use of linguistic information. An experimental system which translates spoken Japanese into English (SL-TRANS) has been implemented. The translation method consists of analysis, transfer and generation processes.A new method incorporated in the system analyzes the expressions of linguistic intention meanings in utterances. In applying this translation method to a goal-oriented dialogue corpus, involving inquiries and explanations regarding an international conference, the experimental system shows advantages for translating Japanese dialogues into English.