1	Semantics (An overview)
2	Outline Basic principles Denotation, meaning and reference Semantic categories Levels of description Semantic phenomena Semantic theories The syntax/semantics interface Computational semantics
3	Basic principles
4	Premises Mind is innately structured, modular: subparts, particular functions, domains Language module exists Lang acq is central puzzle (learnability, innateness, universal grammar) Semantics is a formal system (isolatable, describable, autonomous) Knowledge of language is modular (phonology, morphology, syntax, semantics) Crosslinguistic semantic regularities exist
5	Intuitions Competence (grammaticality, universal properties of language, data) Ungrammaticality: “this can’t be a sentence” Anomaly: “this sentence doesn’t make sense” Colorless green ideas sleep furiously. Categories: classes of words that behave similarly POS (tags), function/content wrds Constituents: word sequences that belong together phrases, clauses, sentences
6	Semantics What components of linguistic processing contribute to meaning? Characterization of the meaning of (parts of) utterances (word/phrase/clause/sen-tence) To what extent can the meaning be derived (compositionally)? ambiguous? Formalisms: networks, models, scripts, schemas, logic(s) Non-literal use of language (metaphors, exaggeration, irony, etc.)
7	Caveats Not everybody agrees on the facts Accounts are often heterogeneous Other related areas are often excluded Prosody Pragmatics Culture Historical aspects of language change Widely divergent approaches, theories exist
8	Denotation, meaning and reference
9	Approaches to meaning Referential (denotational) Relating symbols to external objects Logic, mathematics, models Psychological (mentalist) Relating symbols to internal objects AI, psycholinguistics, semantic representation Pragmatic (social) Communication as a social activity Interactions, agency, conventions
10	Denotation Meaning via reference Configure symbols: characterize relationships between objects Linguistic reference Proper nouns: individuals NP’s Individuals (or groups/collections thereof) Substances, actions, abstract entities But: nonreferentiality A/some/every/no student sneezed. The man who can lift this stone is stronger than an ox.
11	Sense and reference Expressions have both: a reference: what it means on a given occasion (retinal image, subjective) a sense: ways in which the reference is presented (lens image, objective) Reference: actual real-world objects, individuals, classes (extension) U.S. President: {Washington, Clinton, Bush, …} Sense: inherent meaning, concepts, thoughts (intension) U.S. President: elected leader of the U.S.
12	Referential indices John_i saw him_j. John_i saw himself_i. John_i saw him_*i/j. John_i’s father_j saw him_i.
13	More coreference Behind [Mary]_i [she]_i heard the snake. [Herself]_i is proud of [her]_i. If [that jerk]_i calls, tell [Tom]_i I’m busy tonight. [He]_i insists that [the electrician]_i found nothing wrong. [Mary]_i told [John]_j that [they]_/j/i/k were assigned clean-up duty.
14	Referent resolution Syntactic coreference principles (Binding Principles A-C, morphological features, distance) Other methods The Hobbs algorithm: ordering of syntactically-derived potential antecedents from previous utterances The centering algorithm: cache of 2-3 previous sentences, stack-based memory, preference weighting among possible antecedents
15	Basic meaning categories Truth value (adequacy?) Concepts: basic entities Inventories Properties: attributes predicated of concepts Intensity Incommensurability Events: actions, states, processes Internal structure Propositions: predicated assertions
16	Meaning and literality Literal meaning Representation of states of affairs in the world Decontextualized as much as possible Objective, focused, structured The approach we will strive to follow Implicational meaning What the speaker intended or what the hearer’s expected response is Context is crucial to interpretation Negotiable, subjective, variable Not what our focus will be on
17	Other aspects of meaning Expressive meaning Connotation, emotion, feelings Prototypes Most popularly evoked exemplars of a given concept Stereotypes Over-used (often incorrect) exemplars of a given concept Evoked meaning Dialects (geographical, temporal, social) Register (discourse field, tenor, mode)
18	Emotive/evaluative meaning I am firm. You are obstinate. He is a pig-headed fool. Fairer sex, female, broad Between jobs, out of work, on the dole
19	Idioms Non-compositional collocations Severe lexical constraints Off his rocker/rocking chair Gnashing of teeth/molars Shot herself in the foot/*toe Vary crosslinguistically
20	Semantic categories
21	Entities What nouns traditionally refer to Enumerability, discreteness, identifiability Fundamental properties: Specificity Boundedness Animacy Sex and gender Kinship Social status Physical properties Function
22	Entities: specificity Uniqueness, degree of individuation, relative singularity Ambiguity I eat a hamburger every day. (specific, non-specific, generic readings) Related effects: mood (Spn, Frn, etc.) Implies the referent can be uniquely determined Interactions Given/new status in discourse Knownàreferentially accessibleàspecificà definite
23	Entities: boundedness Count vs. mass Enumerability vs. amorphousness Boundedness Is inherent, not derived from the context Properties of internal structure: Bounded: heterogeneous, unexpandable, replicable Unbounded: not Ambiguities The gas escaped. (un-/bounded)
24	Entities: other properties Animacy: basic biological definition In some languages other criteria sometimes apply: topicality, salience, culture, discourse Person: information about participating relevant parties 1^st: self, selves (I, we) 2^nd: you (thou, you, y’all, yunz) 3^rd: others (he, she, it, they, them) Further distinctions: Singular/plural Inclusive/ exclusive distinction Distal (distance-based) distinction
25	Entities: other properties Case: marking an entity’s role in a sentence Nominative=subject, Accusative=direct object, Dative=indirect object, Genitive=possession, Instrumental=means, method, Locative=location (Russian: stol/stola/stolu/stol/stolom/stolje) Varies crosslinguistically (mostly gone in English) Physical properties Interioricity (solids/outlines), size (large/small) Extendedness Dimensionality/shape Direction, extension: vertical, horizontal
26	Entities: other properties Social status Social relations between entities Functions in various processes Here: focus on morphosyntactic honorifics Gender: grammatical category (formal) Sex: inherent biological class (semantic) Pronominalization Subject/object, impersonal/personal, reflexive/reciprocal, etc. Functional role: (co)reference, anaphoric, etc.
27	Entities: classifiers Semantic particles used when counting N’s -bai (time), -bun (part), -chaku (clothing), -choo (beancurd), -dai (vehicle), -fuku (puff, cup), -hai (cup), -hatsu (ammunition round), -hen (volume, chapter, …), -hiki (small animal), -hon (cylindrical object), -ma (room), -shu (poem), -wa (bird), -zen (bowl of rice) Usually based on salient properties of the item counted Wide variation crosslinguistically English has “collective nouns”
28	Kinship relations Fundamentally relational Related properties: gender, age Parameters Consanguineal / affinal Lineal / collateral Generation Directionality Removal Ambiguity My grandfather visited me. (matrilineal, patrilineal)
29	Events Relatively temporal relation in conceptual space States/conditions of existence, processes, unfoldings Actions, executed processes inherently tied to change Process: series of states constituting a phenomenon Relationality: change is essential to identifying events Temporality: time is a crucial element
30	Acts vs. States Stative events Internally uniform Scope: event as a totality More stable temporally, no internal dynamics Less sensitive to temporal distinctions Nonstatives (acts) Heterogeneous, internally structured Scope: components Substates involve more temporal change, dynamism Usually nonpresent tenses
31	Events: resultatives Inchoatives Denote interval between two intervals Boundary crossing into a new condition Can’t distribute to moments Resultatives Complex event: act + change-of-state Not interruptible Don’t saturate down to the moment Ambiguities This shrimp digests easily. (inchoative middle, resultative active)
32	Events: voice Active Logical agent realized as structural subject Passive Logical agent realized obliquely Middle Structural subject of intransitive is the logical object Constraint(s): How affected the subject is: anomaly when affectedness is low Component of becoming: action must be incohative Ambiguity: The door was opened.
33	Events: tense Information dealing with temporal (time) properties Past, future, present manger+ai (Frn); sing+s Remoteness is specified in some languages Not just verbs in some languages tu+pus (Lus)
34	Events: causation Subclass of acts; verbal=causatives Relation of determination between 2 events A language universal; usually verbal Conveyance (e.g. vehicular: drove, trucked) Manner knocked, pounded, hammered (intensity, speed, …) Cause Often inherently encoded kill à cause + become + dead roll à cause + displacement + circular Very periphrastic in other languages
35	Events: transitivity Information dealing with how much the verb transfers the action, and to whom Transitive: action is transferred Intransitive: action isn’t transferred Ditransitive: direct and indirect object Causative: some entity is made/caused to do something Some verbs alternate
36	Space and location Space is relational: dependency between 2 entities/events Located object, reference object Relations can be formalized Location: spatial fixedness Topological: viewer-independent (coincidence, interiority/exteriority) Projective: viewer-dependent (infer-/super-iority, anter-/posteriority, laterality) Ambiguities My book is on the table. (non-/coincedent contact)
37	Aspect Definition(s) Nontemporal internal contour of an event How an event is distributed through a time frame Patterns within an event’s temporal frame English usually conflates with tense
38	Aspectual types Perfectivity Perfective: complete, unitized, viewable as single bounded whole, internal structure less salient Imperfective: opposite Present tense: rarely perfective Slavic languages: verb form dichotomy for imperfective/perfective
39	Aspectual types Telicity Resultative: dual structure (process+result) Success predicated on built-in goals Necessarily imply previous events Processes that exhaust themselves in their consequences Durativity Durative: necessarily distributed over time Punctual: momentary event having no temporal duration
40	Aspectual types Progressivity In progress, on-line, ongoing Continuous and extended from a point into a larger interval Extending an event from the inside Interactions w/punctual event: iterative Simultaneity, coextensiveness Stativizing dynamic events Non-permanence, contingency Wide linguistic variation
41	Aspectual types Habituality Extends an event from the outside Persistence of an event irrespective of time Indefinite protraction of an event Distribution of an event over several times Not just a case of iteration Interactions: perfectivity, past tense, conditional
42	Aspectual types Iterativity Semelfactive: one act, event Iterative: multiple subevents Event cardinality, cyclicity, dual laterality Other aspects Inceptive (incipient, ingressive) Terminative (egressive) Prospective (intentive) Retrospective Intensive
43	Aspectual interactions Functions Foregrounding, narration: perfective Backgrounding, contextual: imperfective Ambiguities John had eaten the popcorn by 1:00. (event frame punctuality, reference time punctuality) The dog just ran away. (proximal preterite, modal)
44	Basic tense concepts The way an event is explicitly indexed for a time frame Event frame: temporally located event Tense locus: contextual, temporal reference point Ordered relations (distal, proximal) Simple (1 event frame) vs. perfect (2 frames) Often tense is inherited; ambiguity can result The man sitting in that chair was rich. (relative past, relative present)
45	Time intervals Fundamental component to events Time line, number line as analogies, descriptive device, model Moments Subintervals Interval Saturation States è moments Acts è subinterval
46	Tense vectoriality Past tense: movement into the completed; bounded, hypothetical, nonactual, counterfactual events Present tense: slightly extended; performatives, in situ narrative, incompleteness, stativity, genericity Future tense: nonactual, hypothetical, inception, prediction, volition, supposition Some languages are tenseless, almost all have only 2 or 3 basic tense distinctions
47	Levels of semantic description
48	Concept feature decomposition Concepts (entities, events, etc.) composed of primitive, binary features These can be used to classify, distinguish, or identify Man vs. girl vs. filly [Niece,daughter,sister] vs. [nun,woman,girl] [Hen,ewe,cow] vs. [rooster,ram,bull] [Table,chair,pencil] vs. [love,thought,idea] [Table,chair,pencil] vs. [water,dirt,cream]
49	Lexical semantics Word meaning Synonymy: youth/adolescent, filbert/hazelnut Antonymy: boy/girl, hot/cold Word senses Polysemy: 2+ related meanings (bright, deposit) Homonymy: 2+ unrelated meanings (bat, file) Connotation: set of ideas, emotions evoked
50	Hierarchical lexical relations Hypernymy, hyponymy Animal ßà dog ßà beagle Dog is a hyponym (specialization) of the concept animal Animal is a hypernym (generalization) of the concept dog Meronymy Carburetor <--> engine <--> vehicle
51	WordNet: hierarchical lexicon Wide-coverage English dictionary Extensive lexical, concept (word sense) inventory Syncategorematic information (frames etc.) Principled organization Hierarchical relations with links between concepts Different structures for different parts of speech Hand-checked for reliability Utility Designed to be used with other systems Machine-readable database Used as a base/standard by many researchers
52	45 WordNet semantic classes (noun.Tops) noun.act noun.animal noun.artifact noun.attribute noun.body noun.cognition noun.communication noun.event noun.feeling noun.food noun.location noun.group 15 Verb classes verb.body verb.change verb.cognition verb.communication verb.competition verb.consumption verb.contact verb.creation verb.emotion verb.motion verb.perception verb.possession verb.social verb.stative verb.weather
53	Sense delimitation Fuzziness (rich, tall, green, clean) Typicality, prototypes Bird: robin vs. penguin Lexicalization (snow) (glint, glimmer, glitter, gleam, glisten, glow, glare) Inventories
54	Predication Associating property with referent Copular verb Adjectival modifier Relative clause
55	Lexicalization The way basic underlying concepts are lexically realized in a language Wide variation crosslinguistically English: motion (V) + path (PP) vs. Romance languages He swam across the river. Il traversa la fleuve à la nage L1 verb à L2 prepositional phrase L1 preposition à L2 verb Limits exist: “flimped”: kissed someone who is allergic to (e.g. John flimped garlic.)
56	Thematic roles Function of constituents wrt basic position Assignment Verbs assign theta-roles to arguments Prepositions assign theta-roles to objects Role played by each NP in a sentence Agent: entity that performs an action Theme, Patient: entity that undergoes an action Source, Goal, Location, Path, Instrument Experiencer: perceiver of a cognitive stimulus Stimulus: perceived cognitive stimulus Various theories, various roles
57	Diagramming q–role assignment The dog chased the cat up the hill.
58	Basic theta-role concepts Theta criterion Relationship with grammar The syntax/semantics interface (mapping) Often direct, sometimes more transparent Ambiguity The witch made the prince a frog. The ducks floated down the river. The anthrax was found by the robot. I smelled the roses.
59	Expressing motion Spatial: core meaning is displacement Non-stative event structure Figure/ground dichotomy Displacement Source + goal, location Path Trajectory, reference point (ground)
60	Semantic phenomena
61	Semantic relationships (1) Paraphrase: re-express identical content The police chased the burglar. The burglar was chased by the police. I gave the summons to Eric. I gave Eric the summons. Paul bought a car from Sue. Sue sold a car to Paul. It is unfortunate that the team lost. Unfortunately, the team lost. The class will begin at 4:00. At 4:00, the class will begin.
62	Semantic relationships (2) Contradiction: inconsistent, opposites John is a bachelor. John is married. I am happy. I am not happy. Today is Monday. Today is Tuesday. Entailment: S1 necessarily implies S2 is true The park wardens killed the bear. è The bear is dead. Robin is a man. è Robin is a human. Jill and Bill have just gotten married to each other. è Jill and Bill are spouses. David is a Republican. è David is a human.
63	Entailments? This is yellow. This is a pencil. ?This is a yellow pencil. This is big. This is a whale. ?This is a big whale. Lee kissed Kim passionately. ?Lee kissed Kim. ?Kim was kissed. ?Lee touched Kim with her lips. ?Lee married Kim. ?Kim kissed Lee. ?Lee kissed Kim many times.
64	Subcategorization What types of complements a word requires/allows/forbids vanish: ø The book vanished ___. prove: NP He proved the theorem. spare: NP NP send: NP PP proof: CP curious: PP or CP toward: NP Valence, arity, argument structure, frame, adicity
65	Selectional restrictions Close synonymy Small/little I have little/small money. This is Fred, my big/large brother. Animacy My neighbor admires my garden. My car admires my garden. Bill frightened his dog/hacksaw. Implicit objects in English
66	Grammaticalization Realizing semantic information via grammatical categories Morphological information Syntactic information Phonological information Wide variation across languages A major focus of this course
67	Semantic ambiguity More than one meaning possible for: word/phrase/clause/sentence/utterance Lexically, morphologically, syntactically invariant Everyone loves somebody.
68	Non-semantic ambiguity Lexical ambiguity I bought a pen. She bought me a fly. Morphological ambiguity Be careful when drawing these axes. Syntactic ambiguity I saw old men and women. I saw her duck. Visiting relatives can be tedious.
69	Semantic ambiguity examples Everyone here speaks two languages. Three men carried a piano. You may not come to my party. Judy wants to marry a Norwegian. Every professor thinks she is busy. A flag was hanging from every balcony. Mary can’t sing. The witch made the prince a frog. John almost walked to the football game.
70	Modality Factual status of propositional content Semantic information associated with speaker’s attitudes/opinions Domain/scope: whole expression Often overlaps with mood (syntax) Various values: necessity, evidentiality, possibility, requirement, negation
71	Modality (epistemic) Expressing judgment on the factual status and/or likelihood of a state of affairs Connects the speaker to the proposition Possibility/necessity modulated by commitment/evidence Truth relativized to a speaker 2 basic categories: judgments (speculation, deduction) vs. evidentials (overtly qualified) Gradient of certainty (challenge: beneath, open to, above)
72	Modality (deontic) Imposition of a state of affairs on individuals Ambiguity
73	Negation: properties Denying the factual status of parts of a proposition open to challenge Propositional/sentential vs. verbal/phrasal Quantifying distance between actuality and nonactuality Comment on speaker’s commitment to proposition Crosslinguistic properties Explicit encoding in some languages Finer distinctions in irrealis Interactions: tense, aspect, definiteness, etc.
74	Negation & scopal interaction Forms: full/aux verb, affixation, doubling, functional variants Scope: wide/narrow, external/internal, sentential/constituent Scope can be narrowed: cooccurrence, movement Interactions: quantifiers Polarity: questions, conditionals, comparison (underlying negation)
75	Semantic theories
76	Lexical-Conceptual Structure Representation of lexical semantic info Classes: action, process, state, event, property, person, thing Causatives, chassé-croisés, other verb-frame alternations Translation divergence analysis Computer applications (IR, text summarization, MT, etc.)
77	Type-Logical Semantics Very tight coupling of syntax, semantics Draws heavily on formal logic principles (proof theory, lambda calculus) Coupling morphosyntax and semantics Parsing languages with complex morphosyntactic structure (Korean, Turkish, Amerindian languages)
78	Meaning-Text Theory Developed in USSR in 1960’s, 1970’s Focus is on semsyn/morphophon correspondence Theme/rheme structure Highly mathematical, formalist in nature Translation contrastive analysis NL generation (template-driven, from data)
79	Cognitive Grammar Visually, schematically organized Used widely, especially for “exotic” languages No extensive computational implementation
80	One cognitive perspective Grammatical categories reflect temporal stability (moreànoun) Cf. events: rapid change in universe Relative atemporality is more precise Scale of temporal stability Abstract nouns almost always derived (e.g. from verbs) Non-productivity of adjectives
81	Philosophy and semantics Philosophical methods for describing entities, relations, events, tense, aspect Formal semantics: using tools (logic, inferencing) to prove meaning in a formalized, model-theoretic manner Explores structural properties, compositionality, interfaces with other aspects of language Derives from math, theorem proving, computation systems Widely used in current semantics work
82	Linguistic semantics Bottom-up, data-driven, language-oriented approach to semantics Literal meaning approach Empirical field Real-world language examples Structure is crucial to analysis Study of variation, richness of crosslinguistic semantic phenomena Skirts around deep philosophical issues Basic realism as a tenet
83	The syntax/semantics interface
84	Basic assumptions Syntax, semantics are different modules They are (somehow) related Knowing about one helps knowing about another They involve divergent representations Both are necessary for a thorough treatment of language
85	Example applications Anaphor John walked the dog to a store. It barked. Lexical disambiguation I saw a cat doze up the neighbor’s lot. Ellipsis recovery John loves his mother and Jane does ___ too. Modality interpretation Fred may not come to my party. Presupposition He wants ___ to win.
86	Two basic approaches Semantics is interpretive Morphology/syntax is prior Map meaning from: syntaxèsemantics Most current linguistic theories Semantics is directly compositional Developed in tandem with syntax Map meaning while: syntax + semantics
87	Interpretive semantics Map: NP’s à entities, individuals VP’s à functions S’s à T values Relate objects in the semantic domain via syntactic relationships
88	Sample sentence syn/sem
89	Parsing (NL-Soar)
90	Generation (NL-Soar)
91	Computational semantics
92	Goals Describe semantics as an abstract, observable, formal system Develop models of implicit knowledge, cognitive processing, and linguistic information Apply these models in real-world situations
93	Extreme approaches I want to process language no matter how I have to do it. (engineering) No intended cognitive plausibility Airplanes don’t flap their wings. I want to process language just like a human does it. (psycholinguistic) Cognitive plausibility You can’t be like a human otherwise.
94	Semantic representations Ways of representing concepts Basic entities, actions Relationships between them Compositionality of meaning Some are very formal, some very informal Various linguistic theories might involve different representations
95	Sample FUF data element
96	Another FUF example "Saturday night, Karl Malone scored 28 points with his hands." ((cat clause) (tense past) (process ((type material) (effect-type creative) (lex "score"))) (partic ((agent ((cat compound-proper) (gender masculine) (head ((cat person-name) (first-name ((lex "Karl"))) (last-name ((lex "Malone"))))))) (created ((cat measure) (quantity ((value 28))) (unit ((lex "point"))))))) (pred-modif ((instrument ((cat pp) (np ((cat common) (number plural) (possessor ((cat personal-pronoun) (index {^5 partic agent index}))) (head ((lex "hand"))))))))) (circum ((time ((cat date) (day-name ((lex "Saturday"))) (day-part ((lex "night"))) (position header)))))))
97	NITROGEN sample (H / \|possible<latent\| :DOMAIN (H2 / \|obligatory<necessary\| :DOMAIN (E / \|eat,take in\| :AGENT YOU :PATIENT (C / \|poulet\|)))) LATTICE STATS: 383 nodes, 1116 arcs, 18755700 paths, 54 distinct unigrams, 446 distinct bigrams, 2154 distinct trigrams. you may have to eat chicken . you might have to eat chicken . you could have to eat chicken . you could be required to eat chicken . you may be required to eat chicken . you might be required to eat chicken . you may have to be eating chicken . you might have to be eating chicken you could have to be eating chicken . you could be obliged to eat chicken . you may be obliged to eat chicken . you might have to eat a chicken . you could have to eat a chicken . you may have to eat the chicken . you might have to eat the chicken . you could have to eat the chicken . you could be having to eat chicken . you may have to eat the chickens . you might have to eat the chickens . you could have to eat the chickens . you could be required to be eating chicken . you may be required to be eating chicken . you might be required to be eating chicken . Consumptions of an chicken by you could be being requirement . That the consumption of the chicken by you was requirements was possibilities .
98	KANT MT overview
99
100	Modeling syntactic processing NL-Soar cognitive modeling system for natural language Most complete X-bar model consistent with lexical properties, syntactic principles Non-productive partial structures are later discarded Input for semantic processing
101	Modeling semantic processing Also done on word-by-word basis Uses lexical-conceptual structure Leverages syntax Builds linkages between concepts Previous versions used 8 semantic primitives Coverage useful but inadequate Difficult to encode adequate distinctions WordNet lexfile names now used as semantic categories
102	Preliminary semantic objects Pieces of conceptual structure Correspond to lexical/phrasal constructions in syntactic model Compatible pieces fused together as appropriate
103	Collocation constraints Enforce compatibility of pieces of semantic model Reflect limited disambiguation Based on semantic classes Ensure proper linkages, reject improper ones Implemented as preferences for potential attachments
104	Final semantic model Most fully connected linkage Includes other sem-related properties not illustrated here Serves as input for further processing (discourse/dialogue, extralinguistic task-specific functions, etc.)
105	Semantic disambiguation Word sense Choosing most correct sense for a word in context Problem: WordNet senses too narrow (large # of senses) Avg. 4.74 for nouns (not a big problem) Avg. 8.63; high of 41 senses for verbs (a problem) Semantic classes Select appropriate WordNet semantic class of word in context An easier, more plausible task
106	Sem constraint for #29 v-body Most frequent verbs in class: wear, sneeze, yawn, wake up (most frequent) Subjects: People Animals Groups Direct Objects: Body Parts Artifacts Indirect Objects: none Subject Constraint sp {topaccessbodyexternal (state <g> ^top-state <ts> ^op <o>) (<o> ^name access) (<ts> ^sentence <word>) (<word> ^word-id.word-name <wordname>) (<word> ^wndata.vals.sense.lxf v-body) --> (<word> ^semprofile <sempro> + &) (<sempro> ^category v-body ^annotation verbclass + & ^psense <wordname> ^external <subject>) (<subject> ^category ^semcat n-animal + & ^semcat n-person + & ^psense * ^internal empty) }
107	Sample sentence: the woman yawned (basic case: most frequent senses succeed.) Syntax: first tree works. Semantics: v-body & n-person match. v-stative never tried.
108	Example #2: The chair yawned (most frequent noun sense inappropriate) Syntax: chair_verb rejected chair_noun accepted Semantics: chair_verb senses rejected n-artifact incompatible w/ v-body n-person accepted
109	Example #3: The crevasse yawned. (most frequent verb sense inappropriate)
110	THE BEGINNING