{ "paper_id": "A92-1035", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T02:03:45.624905Z" }, "title": "Practical World Modeling for NLP Applications", "authors": [ { "first": "Lynn", "middle": [], "last": "Carlson", "suffix": "", "affiliation": { "laboratory": "", "institution": "U.S. Department of Defense Ft. George G. Meade", "location": { "postCode": "20755", "region": "MD" } }, "email": "lcarlson@a.nl.cs.cmu.edu" }, { "first": "Sergei", "middle": [], "last": "Nirenburg", "suffix": "", "affiliation": { "laboratory": "", "institution": "Carnegie Mellon University Pittsburgh", "location": { "region": "PA" } }, "email": "sergei@nl.cs.cmu.edu" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "A92-1035", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": "Practical NLP applications requiring semantic and pragmatic analysis of texts necessitate the construction of a world model, an ontology, to support interpretation of text elements. Constraints on world model elements serve as heuristics on the cooccurrence of lexical and other meanings in the text, facilitating both natural language understanding and generation. Propositional meanings (defined in the lexicon in terms of links to the world model) trickle down to the text meaning representation as instances of world model entities. Our primary objective in world modeling is to support multilingual applications, so constructing a language-independent ontology is crucial. The word sense view of ontology building leads to proliferation of concepts whenever words in different languages do not \"line-up\" (see EDR 1990), while using a core set of \"primitives\" is limited for large-scale applications, if shades of meaning are to be captured. In our environment, concept acquisition is guided by examining cross-linguistic evidence and representational trade-offs. In other large-scale ontology projects, the separation of lexical from conceptual knowledge is not always clear, as in the Cyc project at MCC, a knowledge base containing millions of facts about the world (Lenat and Guha, 1990) , or the KT system (Dahlgren, 1988) , which classifies commonsense knowledge for English words.", "cite_spans": [ { "start": 1273, "end": 1295, "text": "(Lenat and Guha, 1990)", "ref_id": "BIBREF3" }, { "start": 1315, "end": 1331, "text": "(Dahlgren, 1988)", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "Why Does One Need a World Model?", "sec_num": "1" }, { "text": "In the DIONYSUS project at CMU, the world model, the lexicon and the text meaning representation are closely interconnected, in terms of their content and format. World modeling is supported by the ONTOS system, which consists of a) a constraint language, b) an ontology, or set of general concepts, c) a set of domain models and d) an intelligent knowledge acquisition interface. The basic features of the ONTOS constraint language are as follows (see Carlson & Nirenburg, 1990, for details) . A world model is a collection of frames. A frame is a named set of slots, interpreted as an ontological concept (voluntary-olfactory-event, geopolitical-entity) . A slot represents an ontological property (temperature, caused-by) and", "cite_spans": [ { "start": 453, "end": 492, "text": "Carlson & Nirenburg, 1990, for details)", "ref_id": null }, { "start": 607, "end": 655, "text": "(voluntary-olfactory-event, geopolitical-entity)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Why Does One Need a World Model?", "sec_num": "1" }, { "text": "consists of a named set of facets. A facet is a named set of fillers. Facets refer to the status of property values, e.g.:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Why Does One Need a World Model?", "sec_num": "1" }, { "text": "value actual values of property (e.g., for concept instances) default typical value of a property sem set of\"legal\" values; akin to selectional restrictions A filler is a symbol, number, range, etc. A symbolic filler (prefixed by \"*\") names an ontological concept: (ALL (SUB-CLASSES (value *property *object *event))).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Why Does One Need a World Model?", "sec_num": "1" }, { "text": "In the ONTOS system, a mechanism relating scalar attributes (AGE, TEMPERATURE) to measuring units (TEMPORAL-UNIT, THERMOMETRIC-UNIT) allows scalar information to be converted into a standard format for interlingua representation. The DOMAIN slot of a scalar attribute defines the types of concepts the attribute can describe. In the ATrRIBUTE-RANGE slot, the sem facet specifies an absolute constraint on the range of numerical values the attribute can have, while the measuring-unit facet designates a standard unit for interpreting the constraint:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "(AGE (DOMAIN (sem *object)) (ATTRIBUTE-RANGE (sem (> 0)) (measuring-unit *second) ) )", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "Ontology building has never been a totally independent project in our environment. World knowledge in DIONYSUS has been acquired with the express purpose of using it in a natural language processing system. Knowledge representation requirements of such a system include, in addition to ontology specification, a representation for a lexicon entry and a language for recording the meaning of input text. The interaction between these static knowledge sources and a natural language analyzer is illustrated in Figure 1 .", "cite_spans": [], "ref_spans": [ { "start": 508, "end": 516, "text": "Figure 1", "ref_id": "FIGREF0" } ], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "World modeling decisions about scalar attributes are influenced by the way the lexicon is built and vice versa. In the DIONYSUS system, relative scalar terms like very old, or high~low are not given a separate ontological status. Instead, lexical entries for such words are associated with ontologically motivated constraints on scalar attributes. The language-specific relationship between word-modifier and the language-independent relationship between concept-property is illustrated using the example of fresh-brewed coffee. In the ontology, the concept COFFEE appears as follows:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "(COFFEE (IS-A (value *beverage)) (AGE (sem (> 0)) (default (<> 0 4)) (measuring-unit *hour)))", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "The default facet of the AGE slot expresses a typical range of values for the age of COFFEE, which can be overridden, as long as the absolute constraint is not violated. The measuringunit facet selects an appropriate measuring unit from the class TEMPORAL-UNIT. In the lexicon, the link between a word sense and an ontological concept is established in the SEM zone of an entry (see Meyer et al. 1990 ). In the simplest case, there is a direct link, e.g., between the lexeme + c o f f e e-n 2 (the sen se of coffee, the beverage), and the concept COFFEE. However, adjectives like fresh-brewed, old, etc., which represent relative information about age, are linked indirectly to an ontological concept, via a constraint on the default range of the AGE property for a given class of objects. For example, the SEM zone of lexical entry +fresh-brewed-nl establishes the following linkage:", "cite_spans": [ { "start": 383, "end": 400, "text": "Meyer et al. 1990", "ref_id": "BIBREF4" } ], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "instance-of:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "' ~COFFEE or TEA'' age:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "(range < 0.I)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "The range function calculates a default value for freshbrewed coffee, namely, less than 10% of the ontologically specified default range for the AGE of COFFEE.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "Lastly, we demonstrate informally the interdependence of lexicon, ontology and text meaning representation in DIONY-SUS for the sentence I smelled the fresh-brewed coffee. First, we identify the predicate-argument structure, and record the syntactic pattern information in the SYN-STRUC zone of the lexical entry for smell:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "smell (the sense of 'voluntary perception') entity that performs a voluntary perceptual event: I (SUBJ) entity that is the target of a voluntary perceptual event: coffee (OBJ) Next, links between word sense and ontological concept are created for the open-class lexical items in the sentence. These links are recorded in the SEM zone of the lexical entry, where a correspondence is established between semantic and syntactic roles:", "cite_spans": [ { "start": 170, "end": 175, "text": "(OBJ)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "smell ---+ VOLUNTARY-OLFACTORY-EVENT", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "AGENT: ANIMAL (^$varl) ;links to the SUBJ role THEME:", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "PHYSICAL-OBJECT (^$var2) ;links to the OBJ role The A$varl retrieves the meaning of the lexeme bound to the variable $varl during syntactic parsing, places it in the AGENT role of VOLUNTARY-OLFACTORY-EVENT, and checks to make sure that the ontologically specified constraint (AGEN'I2. ANIMAL) is satisfied.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "Finally, we illustrate the key components of the text meaning representation for a sentence: clause head: voluntary-o i factory-event_l aspect : phase : end duration : prolonged iteration: single vo luntary-o i fact or y-event_l agent: speaker theme: coffee 1 coffee i age: < 0.4 hour", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ontology Building in Context: Scalar Attributes", "sec_num": "2" }, { "text": "We have addressed some of the issues that arise when world modeling is viewed as.a component of knowledge support for natural language processing. In DIONYSUS, the interdependence of ontology, lexicon and text meaning representation is such that ontology acquisition never proceeds in an isolated fashion. The discussion presented here regarding scalar attributes is just one example of ontological decision making in context. We would like to thank the members of the DIONY-SUS project-Ralf Brown, Ted Gibson, Todd Kaufmann, John Leavitt, Ingrid Meyer, Eric Nyberg and Boyan Onyshkevych. Thanks also to Irene Nirenburg and Ken Goodman.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Conclusions", "sec_num": "3" } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "World Modeling for NLP", "authors": [ { "first": "Lynn", "middle": [], "last": "Carlson", "suffix": "" }, { "first": "Sergei", "middle": [], "last": "Nirenburg", "suffix": "" } ], "year": 1990, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Carlson, Lynn and Sergei Nirenburg. 1990. World Model- ing for NLP. TR CMU-CMT-90-121. Carnegie Mellon University.", "links": null }, "BIBREF1": { "ref_id": "b1", "title": "Naive Semantics for Natural Language Understanding", "authors": [ { "first": "Kathleen", "middle": [], "last": "Dahlgren", "suffix": "" } ], "year": 1988, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Dahlgren, Kathleen. 1988. Naive Semantics for Natural Language Understanding. Boston: Kluwer Academic Press.", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "Concept Dictionary. TR 027", "authors": [ { "first": "", "middle": [], "last": "Edr", "suffix": "" } ], "year": 1990, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "EDR. 1990. Concept Dictionary. TR 027. Japan Electronic Dictionary Research Institute.", "links": null }, "BIBREF3": { "ref_id": "b3", "title": "Building Large Knowledge-Based Systems", "authors": [ { "first": "Douglas", "middle": [], "last": "Lenat", "suffix": "" }, { "first": "R", "middle": [ "V" ], "last": "Guha", "suffix": "" } ], "year": 1990, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Lenat, Douglas and R.V. Guha. 1990. Building Large Knowledge-Based Systems. Reading, MA: Addison- Wesley.", "links": null }, "BIBREF4": { "ref_id": "b4", "title": "Lexicographic Principles and Design for Knowledge-Based Machine Translation", "authors": [ { "first": "Ingrid", "middle": [], "last": "Meyer", "suffix": "" }, { "first": "Boyan", "middle": [], "last": "Onyshkevych", "suffix": "" }, { "first": "Lynn", "middle": [], "last": "Carlson", "suffix": "" } ], "year": 1990, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Meyer, Ingrid, Boyan Onyshkevych, and Lynn Carl- son. 1990. Lexicographic Principles and Design for Knowledge-Based Machine Translation. TR CMU- CMT-90-118. Carnegie Mellon University.", "links": null } }, "ref_entries": { "FIGREF0": { "type_str": "figure", "num": null, "uris": null, "text": "3 .-.................................................................................... FORMAT ...................... ~NGUAGE :;IIURCE TEXT ............. ~//.. ANALYZER Interrelationship of Ontology, Lexicon, Text Meaning Representation and Processor in a Natural Language Understanding System. The numbered links in the figure are interpreted in the following manner, h ontological concepts are represented in the ON'rOS constraint language. 2, 3, 4: portions of the ONTOS constraint language, the lexicon entry formal and TAMERLAN are shared. 5: lcxical entries are represented in the lexicon entry format. 6, 7:lexical entries have pointers to information in TAMERLAN and/or information in the ontology. 8: TAMERLAN is a formal language for representing the meaning of NL texts. 9: source text is supplied to the analyzer for processing. 10, 1 h part of the analysis process involves accessing and retrieving syntactic, semantic and pragmatic information storedin thelexicon. 12\" theoutputofsemantic analysis is the representation of text meaning (interlingua text), expressed in TAMERLAN." } } } }