Showing papers on "Semantic similarity published in 1990"

Method and apparatus for intelligent help that matches the semantic similarity of the inferred intent of query or command to a best-fit predefined command intent

Abstract: A data processing system which suggests a valid command to a user when the user enters a question or an erroneous command. The purposes of the various commands executable by the system are stored as a plurality of intents. When the user enters a question or an erroneous command, the system looks up the intent corresponding to it and semantically compares such an intent with other intents. When another intent is found, based on the comparison, to be within a predetermined degree of similarity, the command defined by such other intent is offered as a suggestion to the user.

Using latent semantic indexing for information filtering

Abstract: Latent Semantic Indexing (LSI) is an information retrieval method that organizes information into a semantic structure that takes advantage of some of the implicit higher-order associations of words with text objects. The resulting structure reflects the major associative patterns in the data while ignoring some of the smaller variations that may be due to idiosyncrasies in the word usage of individual documents. This permits retrieval based on the “latent” semantic content of the documents rather than just on keyword matches. This paper evaluates using LSI for filtering information such as Netnews articles based on a model of user preferences for articles. Users judged articles on how interesting they were and based on these judgements, LSI predicted whether new articles would be judged interesting. LSI improved prediction performance over keyword matching an average of 13% and showed a 26% improvement in precision over presenting articles in the order received. The results indicate that user preferences for articles tend to cluster based on the semantic similarities between articles.

Computer-Assisted Coding of Textual Data: An Application to Semantic Grammars

Abstract: This article presents the problems involved in implementing a semantic grammar on a computer. Semantic grammars provide powerful content analysis schemes for collecting data from textual sources (e...

Hypertext writing and document reuse: The role of a semantic net

Abstract: SUMMARY When document components are classified and then recombined during document reuse, a semantic net may serve as the classification language. A theory of analogical inheritance, applied to this semantic net, guides the reorganization of document components. Authors index paragraphs from various sources with node-link-node triples from a semantic net and then use programs to traverse the semantic net and generate various outlines. The program examines node and link names in deciding which path to take. This paper describes how these techniques helped the author to reuse parts of an existing book to write a new one.

A Comparison of Three Forms of the Semantic Differential

Abstract: Rating scales are widely used by researchers to measure people's attitudes to a variety of stimuli, yet little time is spent examining respondents' reactions to the form of the scales used for this purpose. Three forms of the semantic differential were tested to determine whether labelling the scale points affects the way that the scales are used, and to determine which form respondents prefer to use. The scale points were either unlabelled, labelled, or numbered. No significant differences were found in the ratings that were obtained with each form, but participants clearly preferred to use the labelled form. It is suggested that this is the form of the semantic differential to use when surveying a diverse audience; the other forms tested may be more suitable for specialist audiences.

Semantic Processing for Finite Domains

Abstract: List of figures Acknowledgements 1. Problems in semantic analysis of text 2. Previous computational approaches to semantic analysis 3. A domain formalization 4. Inference-driven mapping 5. Results of inference-driven semantic analysis Appendices References.

A Cross-Cultural Examination of Semantic Relations

Abstract: This article reports on a cross-cultural investigation of semantic relations that examines the responses of subjects from 8 different cultures to 15 semantic relations. The semantic relations include antonyms, synonyms, class inclusion, and part-whole inclusion. These were chosen due to their use in prior research, and because linguists and cognitive psychologists regard them as linguistically important. Findings reveal significant cross-cultural agreement on the nature of antonymity, which suggests an argument for a property of language use innate to humans.

Domain description grammar: application of linguistic semantics

Abstract: Domain descriptions should represent more than the characteristics of data and the operations on it. They should be "semantic" in the sense that they may represent information such as the meanings of special terms used in the business, as well as goals and rules. ER models are often described as "semantic data models". However, the correspondence between ER and natural language is through syntactic rather than through semantic constructs. Conceptual modeling languages and knowledge representation techniques are more appropriate for representing domain meaning. Modern research in linguistics, semantics, and artificial intelligence provides valuable insight into basic issues regarding such representations. Domain descriptions must use languages based on generally-accepted linguistic and knowledge representation principles.

Semantic abstraction and anaphora

Abstract: This paper describes a way of expressing syntactic rules that associate semantic formulae with strings, but in a manner that is independent of the syntactic details of these formulae. In particular we show how the same rules construct predicate argument formulae in the style of Montague grammar[13], representations reminiscent of situation semantics(Barwise and Perry [2]) and of the event logic of Davidson [5], or representations inspired by the discourse representations proposed by Kamp [9]. The idea is that semantic representations are specified indirectly using semantic construction operators, which enforce an abstraction barrier between the grammar and the semantic representations themselves. First we present a simple grammar which is compatible with the three different sets of constructors for the three formalisms. We then extend the grammar to provide one treatment that accounts for quantifier raising in the three different semantic formalisms

An attractor neural network model of semantic fact retrieval

Abstract: This paper presents an attractor neural network model of semantic fact retrieval, based on the original semantic network models of Collins and Quillian. In the context of modelling a semantic network, a distinction is made between associations linking together objects belonging to hierarchically related semantic classes, and associations linking together objects and their attributes. Using a distributed representation leads to some generalization properties that have computational advantage. Simulations performed demonstrate that it is feasible to get reasonable response performance regarding various semantic queries, and that the temporal pattern of retrieval times obtained in simulations is consistent with psychological experimental data. Therefore, it is shown that attractor neural networks can be successfully used to model higher-level cognitive phenomena than standard content-addressable pattern recognition.

An attractor neural network model of semantic fact retrieval

Abstract: Presents an attractor neural network model of semantic fact retrieval based on A.M. Collins and M.R. Quillian's (1969) semantic network models. In the context of modeling a semantic network, a distinction is made between associations linking together objects belonging to hierarchically related semantic classes and associations linking together objects and their attributes. Using a distributed representation leads to some generalization properties that have computational advantage. Simulations demonstrate that it is feasible to get reasonable response performance regarding various semantic queries and that the temporal pattern of retrieval times obtained in simulations is consistent with psychological experimental data. Therefore, it is shown that attractor neural networks can be successfully used to model higher-level cognitive phenomena than those modeled by standard content-addressable pattern recognition

The Semantic Score Approach to the Disambiguation of PP Attachment Problem

Abstract: In a Natural Language Processing System which takes English as the source input language , the syntactic roles of the prepositional phrases in a sentence are difficult to identify. A large number of ambiguities may result from these phrases. Traditional rule-based approaches to this problem rely heavily on general linguistic knowledge, complicated knowledge bases and sophisticated control mechanism. When uncertainty about the attachment patterns is encountered , some heuristics and ad hoc procedures are adopted to assign attachment preference for disambiguation. Hence, although the literatures about this topic are abundant, there is no guarantee of the objectiveness and optimality of these approaches. In this paper, a probabilistic semantic model is proposed to resolve the PP attachment problem without using complicated knowledge bases and control mechanism. This approach elegantly integrates the linguistic model for semantics interpretation and the objective characteristics of the probabilistic Semantic Score model. Hence, it will assign a much more objective preference measure to each ambiguous attachment pattern. It is found that approximately 90% of the PP attachment problem in computer manuals can be solved with this approach without resorting to any heuristics-based rules and complicated control mechanism. The mapping between the abstract Score Function paradigm and the real PP attachment problem will be addressed in this paper. Future expansion of the semantic score function for resolving general ambiguity problems is also suggested.

The semantic representation of spatial configurations: a conceptual motivation for generation in machine translation

Abstract: This paper deals with the automatic translation of prepositions, which are highly polysemous. Moreover, the same real situation is often expressed by different prepositions in different languages. We proceed from the hypothesis that different usage patterns are due to different conceptualizations of the same real situation. Following cognitive principles of spatial conceptualization, we design a semantic interpretation process for spatial relations in which our translation system uses semantic features derived from a semantic sort hierarchy. Thus we can differentiate subtle distinctions between spatially significant configurations.

Semantic Holism is seriously false

Abstract: Semantic Holism is the claim that any semantic path from inferential semantics (the indeterminate semantics forced by the classical inference rules of PC) reaches all the way to classical semantics if it is even one step long In our joint paper “Semantic Holism”, Belnap and I showed that some such semantic paths are two steps long, but we left open a number of questions about the lengths of semantic paths Here I answer the most important of these questions by showing that there are infinitely long semantic paths that begin at inferential semantics but that do not even reach classical semantics I do this by showing how to construct such an infinite semantic path from the members of the family of (n−1)-out-of-n-disjunction connectives

Solving ambiguities in the semantic representation of texts

Abstract: One of the issues of Artificial Intelligence is the transfer of he knowledge conveyed by Natural Language into formalisms that a computer can interpret. In the Natural Language Processing department of the IBM France Paris Scientific Center, we are developing and evaluating a system prototype whose purpose is to build a semantic representation of written French texts in a rigorous formal model (the Conceptual Graph model, introduced by J. F. Sowa[10]).The semantic representation of texts may then be used in various applications, such as intelligent information retrieval. The accuracy of the semantic representation is therefore crucial in order to obtain valid results in any subsequent applications. In this article we explain how ambiguities related to Natural Language may be solved by semantic analysis using the Conceptual Graph model.

A Measure of Semantic Relatedness for Resolving Ambiguities in Natural Language Database Requests

Abstract: A measure of semantic relatedness based on distance between objects in the database schema has previously been used as a basis for solving a variety of natural language understanding problems including word sense disambiguation, resolution of semantic ambiguities, and attachment of post noun modifiers. The use of min/max values which are usually recorded as part of the process of designing the database schema is proposed as a basis for solving the given problems as they arise in natural language database requests. The min/max values provide a new source of knowledge for resolving ambiguities and a semantics for understanding what knowledge has previously been used by distance measures in database schemas.

Predicting co-occurrence restrictions by using semantic classifications in the lexicon

Abstract: In this paper we investigate general principles of constructing semantic classifications that yield useful predictions concerning combinatory options of words. Several semantic classes of Russian words are discussed, implemented in an expert system named "Lexicographer", the system being conceived as an aid both in the area of natural language processing and in traditional lexicography. Semantic features proposed regulate co-occurence of verbs with their non-obligatory dependents - such as Modifiers of place or time; Instrumental and Benefactive objects and the like.

Semantic modeling through identification and characterization of objects

Abstract: So-called semantic data models represent the structure of some part of reality, on the one hand, and the structure of the data in some database system, on the other hand. Semantic modeling is an important tool for database design. Several techniques and tools for semantic modeling have been proposed [2, 4]. A new approach to semantic modeling — the ICAROS approach — will be presented here.

Similarity measures for sets of strings and application in chemical classification

Abstract: This paper introduces new ideas for quantification of the similarity between chemical compounds. The method adopted makes use of similarity measures derived through comparison of two strings. The derived data on the similarity are then analyzed and applied in the identification of clusters in which the entities are more homogeneous and similar than those outside a cluster.

A semantic data model for intellectual database access

Abstract: A semantic data model is developed to make the intellectual database access possible. The model design is based on the entity-relationship model from the viewpoint of the management of semantic information. The concept of case grammar is introduced, not only to make the properties of entities distinct, but also to interpret natural-language-like queries effectually. >

The Semantic and Formal Connections between Text Components

Abstract: The textological problems discussed in the present paper are of a certain relevance to natural language processing by automata and particularly to automated retrieval of information inherent in natural language texts The investigations connected with those problems have been undertaken be the author within the frame of the research project Anaphora (cf [4], [5], [6]) and of the authors doctoral dissertation (cf [1], [2])

Non-monotonic Reasoning in a Semantic Network

Abstract: We define semantic networks for representing knowledge as being multilabeled semiordered directed graphs. Node labels allow us to treat the problem of universal and existential quantification. Arc labels are used for treating the "defined classes" and negations.

A semantic interpreter for a transportable command language interface

Abstract: Three theories of representation for semantic knowledge have influenced the design of the semantic interpreter. They are semantic nets, conceptual dependency, and conceptual graphs. A brief overview of each formalism and a discussion of their combined influence on the semantic interpreter are presented. The implementation of a semantic interpreter for a transportable command language interface that accepts natural language input is described. The interpreter is based on the theory that sentences expressing the same concept should have the same representation, that is, conceptual dependency. >