Having Your Cake and Eating It Too: Autonomy and Interaction in a Model of Sentence Processing

Is the human language understander a collection of modular processes operating with relative autonomy, or is it a single integrated process? This ongoing debate has polarized the language processing community, with two fundamentally different types of model posited, and with each camp concluding that the other is wrong. One camp puts forth a model with separate processors and distinct knowledge sources to explain one body of data, and the other proposes a model with a single processor and a homogeneous, monolithic knowledge source to explain the other body of data. In this paper we argue that a hybrid approach which combines a unified processor with separate knowledge sources provides an explanation of both bodies of data, and we demonstrate the feasibility of this approach with the computational model called COMPERE. We believe that this approach brings the language processing community significantly closer to offering human-like language processing systems.Comment: 7 pages, uses aaai.sty macr

Semantic Classification for Practical Natural Language Processing

In the field of natural language processing (NLP) there is now a consensus that all NLP systems that seek to represent and manipulate meanings of texts need an ontology, that is a taxonomic classification of concepts in the world to be used as semantic primitives. In our continued efforts to build a multilingual knowledge-based machine translation (KBMT) system using an interlingual meaning representation, we have developed an ontology to facilitate natural language interpretation and generation. The central goal of the Mikrokosmos project is to develop a computer system that produces a comprehensive Text Meaning Representation (TMR) for an input text in any of a set of source languages. Knowledge that supports this process is stored both in language-specific knowledge sources (such as a lexicon) and in an independently motivated, language-neutral ontology of concepts in the world

In silico analysis on macroalgae metabolites against skin cancer protein, phylogenetic and statistical analysis

Anti-skin cancer potential of the macroalgae of Halimeda spp was tested against a skin cancer protein of 4,5-Diarylisoxazole Hsp90 Chaperone by in silico docking method About 32 secondary metabolites of Halimeda spp. reported from previous studies were checked against the skin cancer protein of Hsp90 using the tool of Arguslab 4.0.1. To find out the relevance among skin cancer and other cancers, a phylogenetic tree was constructed for the skin cancer proteins and other cancer proteins. The association among the retention time, the molecular weight of the tested compounds, and docking run were tested using Pearson correlation analysis by Minitab tool. The result exhibited that most of the tested active principles are possessing considerable binding energy. Among them, the highest was recorded for 1,2-Benzenedicarboxylic acid, butyl octyl ester of -14 kcal followed by Phthalic acid, butyl hexyl ester of -13 kcal. From the remaining four compounds showed -12 kcal, 14 compounds expressed -11 kcal and the other compounds possessed -10, -9, -8, and -4 kcal of binding energy. The phylogenetic tree revealed that the relationship of skin cancer having 100% similarity with other cancer protein of wild and home animals, 96% similarity with oral, lung and cervical cancers and 90% similarity with breast cancer protein in human. The correlation analysis showed that the positive association among the retention time, molecular weight of the compounds, and docking run. This study concludes that the Halimeda spp is the right candidate for culminating skin cancer and recommends further studies to establish the potential

Syntax-Semantics Interaction in Sentence Understanding

Natural language is the primary mode of human communication. Developing a complete and well-specified computational model of language understanding is a difficult problem. Understanding a natural language sentence requires the application of many types of knowledge, such as syntactic, semantic, and conceptual knowledge, to resolve the many types of ambiguities that abound in natural language. Most unresolved issues in both psychological and computational modeling of sentence understanding are concerned with the questions of when should each of the various types of knowledge be applied in processing a sentence and how should the different types of knowledge be integrated to select unique interpretations of sentences. In this work, we have developed a model of sentence understanding called COMPERE (Cognitive Model of Parsing and Error Recovery). Our model was built on the hypothesis that a sentence processor has an architecture with separate representations of the different types of knowledge but a single unified process that integrates the different types of knowledge. We have shown that such an architecture addresses the modularity debate by demonstrating how the same sentence processor can produce seemingly modular behaviors in some situations and interactive behaviors in other situations. We have also shown how the unified arbitrating process can not only resolve both syntactic and semantic, lexical and structural, ambiguities, but can also recover from its errors in both syntactic and semantic ambiguity resolution. The unified process can also explain the temporal dependencies in syntax-semantics interactions. It shows how certain decisions are made early and others delayed until further information becomes available. We have developed a parsing algorithm called Head-Signaled Left-Corner parsing to identify the time course of points in the sentence where decisions are to be made. This algorithm decides when to make a commitment and when to delay a syntactic attachment. We have also developed a simple arbitration algorithm for combining information coming from multiple knowledge sources and for resolving any conflicts between them. In addition we have developed a uniform representation of syntactic and semantic interpretations using what are called intermediate roles. These intermediate roles not only aid the dynamic integration of knowledge types by the unified arbitrator, they also provide a declarative record of the intermediate decisions made in syntax-semantics interactions to enable the processor to recover from its errors through repair rather than complete reprocessing. We present a theoretical framework for formal analyses of the performance of sentence processors in various situations. These analyses indicate that the HSLC parsing algorithm, along with incremental interactions between syntax and semantics controlled by the unified arbitrator, reduces the amount of local ambiguity and working memory requirements in processing a sentence. We also present certain psychological predictions made by the COMPERE model

A Theory of Interaction and Independence in Sentence Understanding

Developing a complete and well-specified computational model of human language processing is a difficult problem. Natural language understanding requires the application of many different kinds of knowledge such as syntactic, semantic, and conceptual knowledge. To account for the variety of constructs possible in natural languages and to explain the variety of human behavior in sentence understanding, each kind of knowledge must be applicable independently of others. However, in order to efficiently resolve the many kinds of ambiguities that abound in natural languages, the sentence processor must integrate information available from different knowledge sources as soon as it can. Such early commitment in ambiguity resolution calls for an ability to recover from possible errors in commitment. In this work, we propose a unified-process, multiple knowledge-source model of sentence understanding that satisfies all the constraints above. In this model, syntactic, semantic, and conceptual knowledge are represented separately but in the same form. The single unified process utilizes all knowledge sources to process a sentence. The unified process can resolve structural as well as lexical ambiguities and recover from errors it might make. We show that this model can account for a range of human sentence processing behaviors by producing seemingly autonomous behavior at times and interactive behaviors at other times. It is efficient since it supports interaction between syntactic, semantic, and conceptual processing. Moreover, the model aids portability between domains by separating domain-specific knowledge from general linguistic knowledge. We also present an early commitment, expectation-driven, bottom-up theory of syntactic processing that permits us to unify syntactic processing with semantic processing. We show several illustrative examples of ambiguity resolution and error recovery processed by our prototype implementation of the theory in a program called COMPERE (Cognitive Model of Parsing and Error Recovery)