Designing an Anaphora Resolution Algorithm for Route Instructions

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way path junction road street exit_way branch fork bend turn crossing crossroads t_junction junction turning road street branch fork roundabout exit_way road street Figure 1: ‘ISA’- and ‘PART_OF’-relations between concepts in the ontology As in the ontology the concept thing is subdivided into abstraction and entity the notion of A- and I-Incompatibility is relatively easily to represent. All subtypes of the concept abstraction are I-incompatible, while all subtypes of the concept entity are A- incompatible. The information about the relational order between the concepts is explicitly stored in the ontology but it is also retrievable from the lexicon since the lexical entry of each word contains the conceptual hyper type of the word. Additionally, the lexical entries store closely related concepts (concepts in the same box) and they keep the information about the class of adjectives which are likely to modify the word. As the domain is quite restricted and since there are not many adjectives, the classes of adjectives are small (cf. adjective classification in the lexicon, Appendix 3) and the number of constraints derived from adjective specification is diminutive. But due to the organization of ontology (horizontal relations) the algorithm is able to indicate inconsistent adjective specification. 1) go to the big building on your right 2) then it is the next small building on your left 30
If, as in the example above ‘the building’ mentioned in the route instruction is specified as being big, the algorithm will not resolve the subsequent anaphor to ‘the big building’ because due to the semantic filtering the antecedent of the anaphor is constrained to as being small (and in addition to this ’it’ in the final sentence usually refers to the destination). Moreover, because of the PP-specification the antecedent of the anaphor needs to be ‘on the left hand side of the robot’. To filter this information, reasoning about the specification given in the PP is necessary. Even though this inference process is not implemented yet, the algorithm works as if such a reasoning technique exists. This shows that the predicative context of the anaphor provides information necessary to correctly resolve it. For retrieving this information and defining constraints for the antecedent, the RIAR-algorithm uses six selectional restriction rules and two rules dealing with spatial inference processes (see Appendix 2). The first rule deals with Equation Constructions where the pronominal referent is equated either with an abstract object or with a concrete, individual referent. So is it more likely that the anaphor in the x-position in: x is a suggestion refers to an abstract object while the anaphor in: x is a long street refers to a concrete, individual entity. In equation constructions the anaphor and the complement-NP of the equation construction are entities of the same concept type. Since the anaphor in the previous example occurs in an equation construction the properties of this anaphor are X(sg., [BUILDING], [in(left_region)], [next, small], 2.1). There are two special rules which define how the algorithm handles Spatial Inference given by spatial PPs. The first one controls how spatial relation given in PPs is interpreted. In general, expressions like ‘on your left/right hand side’ or abbreviations of this expression like ‘on your left/right’ or ‘on the left/right’ are interpreted with respect to the actual position of the robot. If such a spatial PP is used to specify an entity, the feature of PP-specification is: [in(left_region)] which means that the entity is located in the left_region of the robot. If the spatial PP is used in a genitive construction like: “the left hand side of the street” where the PP specifies the subsequent noun, the feature of PP-specification is filled as follows: 31
Page 1 and 2: Designing an Anaphora Resolution Al
Page 3 and 4: demonstrative pronouns and 86% of
Page 5 and 6: 1. Introduction The aim of the proj
Page 7 and 8: 2. Background and Related Work As t
Page 9 and 10: concept in the discourse, so they a
Page 11 and 12: discourse while abstract entities a
Page 13 and 14: entities ‘in focus’ while demon
Page 15 and 16: Synchronising units are used to ind
Page 17 and 18: leads to the insertion of a new ent
Page 19 and 20: The discourse model of the PHORA-al
Page 21 and 22: According to Poesio & Modjeska (200
Page 23 and 24: 3. RIAR- and SR-Algorithms In this
Page 25 and 26: Discourse Model at unit(1): origin=
Page 27 and 28: Prepositional phrases like ‘to th
Page 29: position is the number of the updat
Page 33 and 34: abstract object (eventuality). Thus
Page 35 and 36: unit as the anaphor and introduced
Page 37 and 38: incompatible, which means that it r
Page 39 and 40: mentioned in the first sentence, it
Page 41 and 42: S-List. If the anaphor refers to an
Page 43 and 44: 1. update DM … 2. Selectional Res
Page 45 and 46: RIAR-algorithm creates a new S-List
Page 47 and 48: which preposition is inserted into
Page 49 and 50: 4. Evaluation and Discussion In thi
Page 51 and 52: 4.2 Evaluation of the RIAR- and SR-
Page 53 and 54: 2). Personal pronouns are problemat
Page 55 and 56: difficult since I could not find an
Page 57 and 58: 5. Conclusions and Further Work In
Page 59 and 60: Byron’s algorithm handles this pr
Page 61 and 62: eferents. The IBL-ontology shows wh
Page 63 and 64: This-NP-Anaphor: 9 Correct Incorrec
Page 65 and 66: Bibliography Bugmann, G.; Stanislao
Page 67 and 68: APPENDIX 2 SELECTIONAL RESTRICTIONS
Page 69 and 70: - follow on (IV) Æ sub(human_perso
Page 71 and 72: - up (both) Æ obj(path) - go up th
Page 73 and 74: pizza_hut, tesco) - bottom Æ hyper
Page 75 and 76: - street Æ hyper(path), related(ro
Page 77 and 78: Update Discourse Model For each uni
Page 79 and 80: Actions: construct A-List at unit(U
Page 81 and 82:
- the museum is on the left / turn
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position: behind the roundaboutinco
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turn right Æ TURN-Action: create i
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path1(sg., PATH, [in(left_region)],
Page 89 and 90:
2. Selectional Restrictions: (7) Sp
Page 91 and 92:
3. properties of anaphor: X(sg., TR
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3. properties of anaphor: X(sg., PA
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destination not in final sentence b
Page 97 and 98:
6. construct S-List:{Plymouth_unive
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3. a) From my flat to Waverly Stati
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Personal Pronoun: 21 Correct Incorr
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Designing an Anaphora Resolution Algorithm for Route Instructions

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