We propose a method for extracting semantic orientations of words: desirable or undesirable. Regarding semantic orientations as spins of electrons, we use the mean field approximation to compute the approximate probability function of the system instead of the intractable actual probability function. We also propose a criterion for parameter selection on the basis of magnetization. Given only a small number of seed words, the proposed method extracts semantic orientations with high accuracy in the experiments on English lexicon. The result is comparable to the best value ever reported. 1

This paper proposes a method to segment a set of models consistently. The method simultaneously segments models and creates correspondences between segments. First, a graph is constructed whose nodes represent the faces of every mesh, and whose edges connect adjacent faces within a mesh and corresponding faces in different meshes. Second, a consistent segmentation is created by clustering this graph, allowing for outlier segments that are not present in every mesh. The method is demonstrated for several classes of objects and used for two applications: symmetric segmentation and segmentation transfer. Key words: Mesh segmentation, Mesh analysis 1.

Ontologies provide advantages of knowledge reusability, sharing, and greater robustness when used to build large knowledge-based applications. Unfortunately, translating between English statements and a specific ontology requires skill in knowledge engineering and an understanding of formal logic and the ontology itself. A knowledge engineer must be familiar with the concepts in the ontology, the fine distinctions between terms, and the specific way the ontology conceptualizes the world. We are developing a tool, CELT (Controlled English to Logic Translation), to enable non-programmers to add knowledge expressed in terms of an ontology. CELT is an automatic translation tool to convert controlled English to KIF formulas using ontologies built with the Suggested Upper Merged Ontology (SUMO). WordNet provides a base lexicon and a default preference for word senses. We do not expect CELT to obviate the need for knowledge engineers but to instead better leverage their time, as current machine translation tools assist professional human translators. CELT uses Discourse Representation Theory to handle the translation of multiple sentences, the use of logical quantifiers, and the resolution of anaphoric referents. Individual sentences are parsed using a Definite Clause Grammar augmented with feature grammar extensions. CELT is domain-independent but can be customized for particular domains by providing domain-specific ontologies and lexicons. The lexicons can specify both technical terms and domain-specific preferred word senses for common terms. CELT translates sentences to assertions and queries for a first-order logic theorem prover. 1

Spoken language generation for dialogue systems requires a dictionary of mappings between semantic representations of concepts the system wants to express and realizations of those concepts. Dictionary creation is a costly process; it is currently done by hand for each dialogue domain. We propose a novel unsupervised method for learning such mappings from user reviews in the target domain, and test it on restaurant reviews. We test the hypothesis that user reviews that provide individual ratings for distinguished attributes of the domain entity make it possible to map review sentences to their semantic representation with high precision. Experimental analyses show that the mappings learned cover most of the domain ontology, and provide good linguistic variation. A subjective user evaluation shows that the consistency between the semantic representations and the learned realizations is high and that the naturalness of the realizations is higher than a hand-crafted baseline. 1

We propose a modular approach to paraphrase and entailment-rule learning that addresses the morphosyntactic variability of lexical-syntactic templates. Using an entailment module that captures generic morpho-syntactic regularities, we transform every identified template into a canonical form. This way, statistics from different template variations are accumulated for a single template form. Additionally, morpho-syntactic redundant rules are not acquired. This scheme also yields more informative evaluation for the acquisition quality, since the bias towards rules with many frequent variations is avoided.

Many algorithms extract terms from text together with some kind of taxonomic classification (is-a) link. However, the general approaches used today, and specifically the methods of evaluating results, exhibit serious shortcomings. Harvesting without focusing on a specific conceptual area may deliver large numbers of terms, but they are scattered over an immense concept space, making Recall judgments impossible. Regarding Precision, simply judging the correctness of terms and their individual classification links may provide high scores, but this doesn’t help with the eventual assembly of terms into a single coherent

this paper first gives an overview of the tiers and then discusses each of them in turn. It sketches how a computational model of ontology could be built and draws some conclusions about its usefulness.

kumaran,haritsa¡ Abstract. In an increasingly multilingual world, it is critical that information management tools organically support the simultaneous use of multiple natural languages. A pre-requisite for efficiently achieving this goal is that the underlying database engines must provide seamless matching of text data across languages. We propose here SemEQUAL, a new SQL functionality for semantic matching of multilingual attribute data. Our current implementation defines matches based on the standard WordNet linguistic ontologies. A performance evaluation of SemEQUAL, implemented using standard SQL:1999 features on a suite of commercial database systems indicates unacceptably slow response times. However, by tuning the schema and index choices to match typical linguistic features, we show that the performance can be improved to a level commensurate with online user interaction. 1

As the use of Electronic Medical Records (EMRs) becomes more widespread, so does the need for effective information discovery within them. Recently proposed EMR standards are XML-based. A key characteristic in these standards is the frequent use of ontological references, i.e., ontological concept codes appear as XML elements and are used to associate portions of the EMR document with concepts defined in a domain ontology. A rich corpus of work addresses searching XML documents. Unfortunately, these works do not make use of ontological references to enhance search. In this paper we present the XOntoRank system which addresses the problem of ontology-aware keyword search of XML documents with a particular focus on EMR XML documents. Our current prototypes and experiments use the Health Level Seven (HL7) Clinical Document Architecture (CDA) Release 2.0 standard of EMR representation and the Systematized Nomenclature of Human and Veterinary Medicine (SNOMED) ontology, although the presented techniques and results are applicable to any EMR hierarchical format and any ontology that defines concepts and relationships.

Natural languages encode gender distinctions in various ways. We investigate the differences between English and Hebrew in this respect, our departure point being the relations that are defined between the feminine and the masculine realizations of nouns in the English WordNet. We define a number of distinct classes of English nouns which differ in the way they realize gender distinctions. We then define similar classes of Hebrew nouns and show how to map the Hebrew nouns (and relations defined over them) to the English structure. This establishes a systematic assignment of Hebrew nouns to WordNet synsets, which is consistent with the ideas underlying multilingual extensions of WordNet. The main result is a consistent Hebrew WordNet which is aligned with the English one, but an additional contribution is a set of desiderata for the correct encoding of (systematic) semantic differences among languages. 1