SEMANTICS. Introduction Meaning Representation Meaning Interpretation. PLN semántica 1 - PDF

Description
SEMANTICS Introduction Meaning Representation Meaning Interpretation PLN semántica 1 Introduction 1 Semantics is about the meaning of the sentences. Semantic Interpretation is the process of obtaining

Please download to get full document.

View again

of 29
All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
Information
Category:

Psychology

Publish on:

Views: 15 | Pages: 29

Extension: PDF | Download: 0

Share
Transcript
SEMANTICS Introduction Meaning Representation Meaning Interpretation PLN semántica 1 Introduction 1 Semantics is about the meaning of the sentences. Semantic Interpretation is the process of obtaining The sentence meaning. Sentence in NL Logic Form Contextual Information Determining the correct meaning for each word Combination of the meaning of the words to build a logical from Meaning Representation PLN semántica 2 Introduction 2 The semantic representation of an object is obtained from the semantic interpretation of its components. The semantic interpretation process must be based in a theory, not in an ad-hoc process. This theory must support: - lexical and syntactic ambiguity - complex phenomena: negation, quantification, inferences, etc. An interface mechanism between sintax and semantic must be defined. PLN semántica 3 Composition of meaning from the meaning of parts Incorporing the feature sem to each CFG rule S NP loves NP S[sem=loves(x,y)] NP[sem=x] loves NP[sem=y] The meaning of S is obtained from the meaning of the NPs S loves(x,y) S died(x) NP x V loves VP NP y NP x V kicked VP NP the bucket PLN semántica 4 Composition of meaning from the meaning of parts Adding sem feature Example Rule 1 S NP loves NP S[sem=loves(x,y)] Example Rules 2 NP[sem=x] loves NP[sem=y] V[sem=loves] loves VP[sem=v(obj)] V[sem=v] NP[sem=obj] S[sem=vp(subj)] NP[sem=subj] VP[sem=vp] Example of resulting analysis George loves Laura sem=loves(laura)(george) PLN semántica 5 Composition of meaning from the meaning of parts Simple semantic interpretation IS bottom-up Grammar is in CNF Each node two sons: 1 function & 1 argument To obtain semantic interpretation: application of the function to argument PLN semántica 6 Meaning Representation1 input: Who organizes the party? logical form: (question (referent (X)) ( X instance (X, persona) (el1 (Y instance(y, party)) ( Z instance(z, organizes) present(z) value_prop(z, agent, X) value_prop(z, patient,y))))) PLN semántica 7 Meaning Representation 2 This form includes four different types of knowledge: Logical Conceptual Speech act Pragmatics The semantic formalism must support these different types of knowledge. PLN semántica 8 Representations Based on Logic A finite set of functions with arguments. A finite set of predicates (functions that return a boolean value)with arguments. A finite set of constants and variables. A finite set of logical connectors. A finite set of quantifiers, that will be applied over the predicates. PLN semántica 9 Objects Three types: Boolean True or false Entities Classes and their elements. For example, NPs Especifications of space and time Functions PLN semántica 10 Functions 1 Use of expressions in functions names the cat g cat(g) adjetives The cat black and fat g cat(g) black(g) fat(g) Other modifiers Peter's cat g cat(g) belongs_to(g,peter) PLN semántica 11 Functions 2 Arity of predicates Unary predicate: over entities g cat(g) Binary predicate over 2 entities g belong_to(g,pepe) Binary predicate over 2 unary predicates over entities Almost all cats see. almost( g cat(g), g see(g)) PLN semántica 12 Basic problems of Representation Quantification Intrasentencial reference Subordination Negation Conjuntion Ambiguity A cat eats a fish ( X:cat ( Y:fish eats (X, Y))) PLN semántica 13 Meaning Representation 3 Which information has to be represented? All information that can be obtained from the sentenced and it could be useful. For example, Allen states that to represent a nominal phrase four different types of information is necessary: 1 Operator 2 Variable 3 Type 4 Modifiers PLN semántica 14 Meaning Representation 4 The boy (DEF/SING N1 BOY) The big boy (DEF/SING N1 BOY (BIG N1)) Each boy eat a big cake (PAST C1 EAT (AGENT C1 (EACH N1 BOY)) (THEME C1 (INDEF/SING P1 CAKE (BIG P1)))) JOHN ARRIVED AT THE STATION (PAST L1 ARRIVE (AGENT L1 (NAME J1 PERSONA JOHN )) (TO-LOC L1 (DEF/SING E1 STATION))) PLN semántica 15 The logical forms are associated with the verb, the central part of the sentence. They included the modifiers representing the different cases: agentive, instrument, thema, patient, locative, temporal, etc... Representing meaning 5 Juan rompió la ventana con el martillo agente tema el viento rompió la ventana instrumento (no agente) tema instrumento PLN semántica 16 Juan murió paciente (no agente) The Semantic Networks 1 Labeled directed graphs nodes == concepts (classes or types) / objects (instancias) edges == binary relations ( binary predicates) between concepts. Quillian (1968), Simmons (1973) Knowledge Representation Systems baseds on semantic networks NePS (Shapiro), Partitioned network ( Hendrix) KL-ONE (Brachman) Global organization of the Knowledge Base. Inference rules (basically, inheritance) PLN semántica 17 The Semantic Networks 2 Advantages Visibility. Associative representation. Efficient access. Appropiate for knowledge searching and inference. Representation of both general and specific knowledge. Supporting complex matching processes. PLN semántica 18 The Semantic Networks 2 Disadvantages Representation of relations of arity higher than two is difficult ( unary and binary relations are easily represented). Representation of logic operations such as negation, implication and disjuntion is difficult. Representation of quantification is difficult. PLN semántica 19 Frames 1 Representation of stereotypes Descriptors Classes and instances Descriptors (attributes) and relationships. Facetts trawberrys Semantic objects and relations predefined Not standard objects. PLN semántica 20 Frames Inheritance of properties Other forms of inferences The red car : The car is not completely red but only the external part. one coffee spoon of sugar = the quantity of sugar that corresponds to that in a coffee spoon Procedimental information Different levels of granularity and abstraction 350 gr. of beans, two pieces of fruit, plenty of PLN semántica 21 Frames2 Sets of simple objects: Enumeration: three potatoes, salt and pepper Global reference: Fresh fruit, garlic Quantification: A tee spoon of sugar Disjunction: one big potato or two small ones Not exhaustive lists: Apples, bananas, oranges, etc... PLN semántica 22 Frames Objects not quantified. Mass: 3 Kg de rice Not formal metrics: A cup of rice Not specific quantities: A little bit of salt, some sugar PLN semántica 23 Frames 4 Properties Describing the content: Mature fruit Describing the de form: A big apple Describing what it is not: Olives without bones States, process, actions, success Fry the sliced onion until golden brown PLN semántica 24 Graphs of conceptual dependency Semantic graphs where nodes and edges belong to the set of predefined semantic objects and relationships (Schanck). Understanding a text == following (logical) causal chain The elements in the chain are conceptualizations and are linked by (logic) causal relationships. Schanck s formalism is a dependency grammar. Representation based on actions and associated with deep structure based on: PP Names, PA Adjetives, ACT Actions AA Adverbs PLN semántica 25 Conceptualizations Actor: An actor acts ( agentivo act) PP ACT Goal: An action achieves a goal (objective) ACT O PP Place: Change in the owner of an object: PP ACT R PLN semántica 26 PP Conceptualizations 2 Directive: Initial and final points of an action: ACT D PP PP Instrument: ACT I PLN semántica 27 Predefined Semantic Actions Phisical actions : MOVE, PROPEL, EXPEL, GRASP, INGEST Ingest : An actor X moves an object Y from an external position W to an internal postion (in a phisical body) Z. Z X INGEST O Y D Changes in the state: PTRANS: Physical movement ATRANS: Change in the abstract relationship Instruments: SPEAK: Making a sound ATTEND: Direct a sense towards an estimulus Mental actions: MTRANS: Transfering information MBUILD: Combination PLN semántica 28 W Example Juan is running JUAN JUAN PTRANS O JUAN I rápido MOVER JUAN POSSBY O PIERNAS PLN semántica 29
Related Search
We Need Your Support
Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks