===NLP ANAPHORA 

@inproceedings{kehler-94_common-topics-coherent-situations-interpreting-ellipsis,
  title={Common topics and coherent situations: Interpreting ellipsis in the context of discourse inference},
  author={Kehler, Andrew},
  booktitle={Proceedings of the 32nd annual meeting on Association for Computational Linguistics},
  pages={50--57},
  year={1994},
  organization={Association for Computational Linguistics},
  annote = {
}}


@article{kehler-00_coherence-and-resolution-of-ellipsis,
  title={Coherence and the resolution of ellipsis},
  author={Kehler, Andrew},
  journal={Linguistics and Philosophy},
  volume={23},
  number={6},
  pages={533--575},
  year={2000},
  publisher={Springer},
  annote = {

The case of verb phrase (VP) ellipsis is exemplified in sentence (1).

	(1) Ross likes his mother, and Bill does too.

The stranded auxiliary in the second clause (henceforth, the target clause)
marks a vestigial verb phrase, a meaning for which must be determined
from the meaning of another clause, in this case, the first clause (henceforth,
the source clause). The target clause in sentence (1) may receive one
of two readings, one in which Bill likes Ross’s mother (the strict reading),
and one in which Bill likes his own mother (the sloppy reading).

--1.2. Semantic Handling--

In semantic accounts of VP-ellipsis resolution (Dalrymple et al., 1991;
Hardt, 1992; Kehler, 1993a; Hardt, 1999, inter alia), VP-ellipsis is resolved
at a purely semantic level of representation. Strong evidence has also been
provided to support this view, such as the acceptability of sentences (5)–
(7).

(5) In March, four fireworks manufacturers asked that the decision
	be reversed, and onMonday the ICC did. [ reverse the decision ]
	(from text of Rosenthal (1988), cited in Dalrymple (1991))

(6) Harry used to be a great speaker, but he can’t anymore, because
	he lost his voice. [ speak ] (from Hardt (1993))

(7) I expected Billi to win even when hei didn’t. [ expect Billi to
	win ] (from Dalrymple (1991))

VP-ellipsis is felicitous in sentence (5) despite the fact that the source
clause is passivized, and thus a syntactic structure for the VP reverse the
decision is not available. Sentence (6) is acceptable even though the
referent is evoked by a nominalization. Finally, sentence (7) is felicitous
despite the fact that Condition C predicts unacceptability under a syntactic
account.

For the purpose of this paper, we will use the representation and method
of Dalrymple et al. (1991) as our semantic resolution process. As shown
in Figure 1, a semantic representation for the source clause of sentence (1)
is given in (8).

	(8) likes(Ross, mother(Ross))


As shown in Figure 2, the representation of the target clause contains an
uninstantiated relation that applies to the overt material.

	(9) P(Bill)

In representation (9), P stands proxy for a missing relation, which in this
case corresponds to the elided VP in the syntax. We solve for P by computing
the relation that when applied to Ross results in representation (8);
that is, we solve for P in equation (10).

	(10) P(Ross) = likes(Ross, mother(Ross))

A solution to this equation is given in (11),3

	(11) P = x.like(x, mother(x))

which when applied to Bill in the target results in meaning (12).

	(12) like(Bill, mother(Bill))

One manner in which to view the semantic resolution procedure is as an
anaphoric process (Schachter, 1978; Chao, 1987; Webber, 1978; Hardt, 1993;
Kehler, 1995; Kehler and Shieber, 1997; Lobeck, 1999, inter alia).  Indeed,
VP-ellipsis has the characteristics of being anaphoric, in that it patterns
with other forms of anaphora, such as pronominal reference.

}}

@phdthesis{kehler-95-phd_interpreting-cohesive-forms-in-discourse-inference,
  title={Interpreting cohesive forms in the context of discourse inference},
  author={Kehler, Andrew},
  year={1995},
  school={Citeseer},
  annote = {
}}


---

@phdthesis{root-RL-86-phd-ut_semantics-of-anaphora-in-discourse,
  title={The semantics of anaphora in discourse},
  author={Root, Rebecca Louise},
  year={1986},
  school={University of Texas at Austin},
  annote = {

ABSTRACT

Anaphora may link to discontinuous antecedents, as in "John found a piano
teacher.  They are both fond of Mozart", and links from plural anaphors to
singular antecedents in distributive contexts, as in "Every girl brought a
cake.  Coincidentally, they were all chocolate." Despite the surface
variation, it is argued here that a uniform account of these constructions is
possible. An analysis of the semantic properties of discourse anaphora is
presented here which offers a unified explanation of both the truth
conditions and the acceptability conditions of this phenomenon. The analysis
is framed within the context of the Discourse Representation Theory of Hans
Kamp. In this theory, each sentence contributes to the construction of a
representation of the meaning of the discourse. This is done in part through
the introduction of "reference markers" for each noun phrase. Truth is
defined for such a representation in terms of an embedding of the
representation in a model. In this account, an anaphoric link is truthful if
the set of individuals to which the anaphor's reference marker is mapped, is
the same as the set of individuals to which the reference markers of the
antecedent(s) are mapped in the course of finding an embedding for the
representation. . This definition formalized, together with the assumptions
that the discourse is true and that there is a principle of semantic number
agreement, constitute the analysis presented here. In addition to the formal
explanations, it is argued that this approach is attractive from a
computational point of view, and an implementation of the leading ideas is
presented.

---

The term anaphor is used here in the general sense of a word which points
back to some previous linguistic or semantic object. This differs from the
usage within Government and Binding Theory in which the term is used more
narrowly to refer to linguistic elements, including empty categories, which
are necessarily bound by an antecedent according to strict syntactic
criteria.

At the discourse level, a syntactic solution does not look 
promising because of the wide variety of surface configurations which are 
possible, especially when plural anaphors are involved. One finds 
straightforward links to antecedents in virtually any noun phrase position. 
(0.1) 

a. John read a book. He liked It. 
b. John asked Mary about her problem. 
   He knew It was bothering her. 

But in addition to these, there are links from anaphors to 
discontinuous antecedents, as in (0.1): 

	a. John found a piano teacher. 
	b. They are both fond of Mozart. 

And there are links from plural anaphors to singular antecedents in 
distributive contexts, as in (0.2): 

	a. Every girl brought a cake. 
	b. They v*re chocolate. 
	* It was chocolate. 

The problem is to characterize what anaphors can be used when; given that
structural relationships between antecedent and anaphor are not operative,
and to specify how these anaphors are interpreted. I will concentrate
initially on discourse anaphora involving quantified noun phrase antecedents.

--1.1 Distributive contexts : Referential vs Bound anaphora--

In the work on anaphora, a distinction is often drawn between what 
is called REFERENTIAL anaphora (1.1), and BOUND anaphora (1.2): 

	(1.1) John said that he vas sick. 
	(1.2) Every man at the party said that he was sick. 

One of the alleged differences between these two is that referential 
anaphora can occur across sentence boundaries, whereas bound anaphora, as 
the name implies, is restricted to sentential contexts since it arises where 
anaphoric elements are treated as variables bound by a quantifier phrase 
within whose scope the anaphor lies. By making the natural assumption, then, 
that quantifier scope does not ordinarily span sentence boundaries, we have an 
account for the grammatically judgments given here for (1.3) and (1.4): 

	(1.3) John was at the party. He was drunk. 
	(1.4) * Every man was at the party. He was drunk. 

But there is another sort of anaphoric link which involves neither 
coreference nor variable binding. This link can be illustrated by the
following: 

	a. Few men were at the party. 
	   They said they were drunk. 
	b. Many men were at the party. 
	   They said they were drunk.		(1.5) 

Before discussing the interpretation which is of interest here, it will 
be useful to mention a few potential sources of confusion. They have to do 
with establishing the referent of the pronouns in these examples and in most 
of the sentences which will be presented here. 

The first is the deictic use of the pronoun. Given an appropriate context,
the first instance of "they" (we will always take the second to be bound by
the first) may pick out something not mentioned in the discourse or may
coincidentally pick out some set similar (or identical) to ones alluded to in
the discourse, such as the few (many) men at the party (or some subpart of
that group) or the many (few) men not at the party (or some subpart of that
group), or any mixed set at all, so long as there is a sufficiently rich
context of previous mention, shared presumptions and finger pointing.

The second source of confusion relates to reference to a generic set. 
The utterance of virtually any noun phrase makes salient the generic set 
typically associated with the bare plural version of that noun phrase, even 
when the original noun phrase can't be, or isn't likely to be, interpreted in a 
generic sense. This set is available to be picked out by an anaphor with out 
specifically being mentioned. The following cases illustrate this: 

a. A cat just walked into the room. 
   They are not very shy, are they? 
		they = cats 
b. The two prisoners won their suit. 
   They'll have better conditions now. 
	   they = prisoners 

c. Most students are taking a gym class. 
   They are pretty easy and it gets you around 
	    the hours requirements, 
		they — gym classes 
   They have to take 9 hours, and this is an easy 
   way to do it. 
		they = students 		(1.7)

Because the quantifier phrase antecedents are not referring 
expressions, it does not make sense, on the surface of it, to speak of the 
anaphora as being preferential to their antecedents. On the other hand, the 
assumption that this is a case of bound anaphora is equally problematic. This 
can be seen by comparing (1.5) with their bound variable near paraphrases in 

	a. Few men who were at the party said they were  drunk. 
	b. Many men who were at the party said they were  drunk. 

There is clearly a change in meaning. Unlike the sentences in (1.7), 
the sentences in (1.5) entail that few (many) men were at the party. Also, in 
the sentences in (1.5), as opposed to those in (1.7), all the men who were at 
the party claimed to be drunk. In (1.7), the relevant set of men making this 
claim is restricted by the quantifier. There is a syntactic difference in this 
form of anaphora as well. Normally, anaphors in English agree with their 
antecedents with respect to number. All of the examples given thus far 
exhibit this property. But while we found the anaphoric link in (1.4) to be 
impossible, the discourse improves greatly when a plural anaphor is used: 

(1.8) Every man from our office was at the party. 
      They were drunk. 

Another problem arises in cases where singular indefinite noun 
phrases are used as antecedents. They seem to act sometimes like referential 
noun phrases and sometimes like quantified noun phrases with respect to the 
anaphors they take. This can be seen in the following: 
(1.9)  A man from our office was at the party. 
       He was drunk. 
(1.10) Every woman met a man from our office at the party. 
       * He was drunk. 
       They were drunk. (1.11) 

Whatever semantic properties license the use of the plural anaphor, they
certainly don't derive solely from the semantic properties of the antecedent
itself, unless one is willing to tolerate a great deal of ambiguity in the
analysis of the singular indefinite.

This work is primarily concerned with providing an explanation for 
the semantic properties associated with the type of anaphora exhibited in 
(1.5), (1.8), and (1.11). 

This work is primarily concerned with providing an explanation for the
semantic properties associated with the type of anaphora exhibited in (1.5),
(1.8), and (1.11). It is like bound anaphora in that there is no obvious
object which can be said to be the antecedent, but it is like referential
anaphora in that the anaphor is a referring expression.

}}

@inproceedings{rahman-ng-11ijc_ensemble-based-coreference-resolution,
  title={Ensemble-based coreference resolution},
  author={Altaf Rahman and Vincent Ng},
  booktitle={Proceedings of the 22nd IJCAI Volume Three},
  pages={1884--1889},
  year={2011},
  abstract = {

We investigate new methods for creating and applying ensembles for
coreference resolution. While existing ensembles for coreference resolution
are typically created using different learning algorithms, clustering
algorithms or training sets, we harness recent advances in coreference
modeling and propose to create our ensemble from a variety of supervised
coreference models. However, the presence of pairwise and non-pairwise
coreference models in our ensemble presents a challenge to its application:
it is not immediately clear how to combine the coreference decisions made by
these models. We investigate different methods for applying a
model-heterogeneous ensemble for coreference resolution. Empirical results on
the ACE data sets demonstrate the promise of ensemble approaches: all
ensemble-based systems significantly outperform the best member of the
ensemble.
}}


@inproceedings{nguyen-poesio-12coling_relational-coreference-resolution,
 author = {Nguyen, Truc-Vien T. and Poesio, Massimo},
 title = {Relational Structures and Models for Coreference Resolution},
 booktitle = {Proceedings of the 24rd International Conference on Computational Linguistics},
 series = {COLING '12},
 month = {December},
 year = {2012},
 location = {Mumbai, India},
 numpages = {10}
 annote = {
Truc-Vien T. Nguyen, M assimo Poesio 1,2

(1) CIMeC, University of Trento, Rovereto (TN), 38068, Italy
(2) University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
{trucvien.nguyenthi,massimo.poesio}@unitn.it

Abstract

Coreference resolution is the task of identifying the sets of mentions
referring to the same entity.  Although modern machine learning approaches to
coreference resolution exploit a variety of semantic information, the
literature on the effect of relational information on coreference is still
very limited. In this paper, we discuss and compare two methods for
incorporating relational information into a coreference resolver. One
approach is to use a filtering algorithm to rerank the output of coreference
hypotheses. The filter is based on the relational structures between mentions
and their corresponding relationships. The second approach is to use a joint
model enriched with a set of relational features derived from semantic
relations of each mention. Both methods have shown to improve the performance
of a learning-based state-of-the-art coreference resolver.

}}


@inproceedings{stoyanov-eisneree-12coling_easy-first-coreference-resolution,
  title =	 {Easy-first Coreference Resolution},
  author =	 {Veselin Stoyanov and Jason Eisneree},
  booktitle =	 {Proceedings 24rd COLING '12},
  month =	 {Dec},
  year =	 {2012},
  location =	 {Mumbai, India},
  annote = {

EXAMPLE:
   Tamara Maschino, a resident of the Clear Lake area of Houston, criticizes
   Bush for his lack of attention to pollution problems from chemical plants
   near her home.

here the phrases [Tamara Maschino] [resident of the Clear Lake area of
Houston] and [her] all have the same referent, while {Bush] and [his] have
the same.

coreference resolution is an NLP area which tries to identify phrases having
the same coreferent.

this paper presents a rule-based approach to coreference resolution, which
starts with the easier approaches.  It is also a detailed analysis of this
work:

abstract
Coreference resolution has traditionally benefited from machine learning
approaches (Soon et al., 2001; Ng and Cardie, 2002; Bengtson and Roth, 2008;
Stoyanov et al., 2009). Surprisingly, however, recent work has shown that
simple rule-based coreference systems can compete with the state-of-the-art
machine-learning-based systems if provided with rich lexical, syntactic,
semantic and discourse information (Haghighi and Klein, 2010; Raghunathan et
al., 2010). In fact, the rule-based system of Raghunathan et al. (2010)
exhibited the top score in the recent CoNLL evaluation (Pradhan et al.,
2011). This system’s innovation is to build the coreference clusters
incrementally, starting with the most precise rules (dubbed sieves by
Raghunathan et al.  (2010)) and use the available coreference information to
guide the less precise sieves.

Ng, V. and Cardie, C. (2002). Improving machine learning approaches to
   coreference resolution.  In Proceedings of ACL, pages 104–111.
Bengtson, E. and Roth, D. (2008). Understanding the value of features for
   coreference resolution. In Proceedings of EMNLP, pages 294–303.
Haghighi, A. and Klein, D. (2010). Coreference resolution in a modular,
   entity-centered model. In Proceedings of NAACL, pages 385–393.
Raghunathan, K., Lee, H., Rangarajan, S., Chambers, N., Surdeanu, M.,
   Jurafsky, D., and Manning, C. (2010). A multi-pass sieve for coreference
   resolution. In Proceedings of EMNLP, pages 492–501.
Stoyanov, V., Gilbert, N., Cardie, C., and Riloff, E. (2009). Conundrums in
   noun phrase coreference resolution: Making sense of the
   state-of-the-art. In Proceedings of ACL/IJCNLP, pages 656–664.

Coreference resolution is inherently a global task: for example, discovering
that Smith and she corefer makes it more probable that Smith corefers with
Jane Smith (i.e., a female name) rather than Jason Smith. Furthermore,
grouping Jane Smith, she and Jason Smith in the same cluster should be
rejected by the clustering algorithm because it results in poor gender and
proper name agreement. Yet incorporating such structured information in
coreference has proven challenging.

There have been several successful attempts to incorporate structured
information through joint inference such as Culotta et al. (2007) and Poon
and Domingos (2008), but they do not explicitly learn parameters tuned to the
inference algorithm used at test time (and the latter relies on a complicated
and expensive inference procedures). The difficulty of inferring globally
consistent clusterings lies in the fact that there are exponentially many
clusterings and producing an "optimal" clustering according to most measures
of global coherence is NP-hard.  In this context, the approach of Raghunathan
et al. (2010) can be seen as a rule-based greedy search for a globally
consistent clustering.

[resolves gender etc in multiple sieves to resolve pronouns]

abt 100 features - set of weights for the feature vector -
weight of a join = w.join_ij

--Features from Stoyanov et al. (2009)--
ProComp, SoonStr, Modifier, PostModifier, WordsSubstr, Pronoun1, Pronoun2,
Definite1, Definite2, Demonstrative2, Embedded1, Embedded2, InQuote1,
InQuote2, BothProperNouns, BothEmbedded, BothInQuotes, BothPronouns,
BothSubjects, Subject1, Subject2, Appositive, RoleAppositive, MaximalNP,
SentNum0, SentNum1, SentNum2, SentNum3, SentNum4plus, ParNum0, ParNum1,
ParNum2plus, Alias, Acronym, IAntes, WeAntes, BothYou, Span, Binding,
Contraindices, Syntax, ClosestComp, Indefinite, Indefinite1, Prednom,
Pronoun, ProperNoun , WordNetClass, WordNetDist, WordNetSense, Subclass,
AlwaysCompatible, WNSynonyms, Quantity, PairType

--Features from Raghunathan et al. (2010)--
IwithinI, Demonyms, ModifierHeadMatch, WhoResolve, WhichResolve

Novel features:
DeterminerHeadMatch, Longer2, LongerPN2, ShorterPN2, Halt

reinforcement learning approach - try to learn good policy -
tried policy gradient / Q-learning - didn't work - supervision is too distant

Stoyanov, V., Gilbert, N., Cardie, C., and Riloff, E. (2009). Conundrums in
   noun phrase coreference resolution: Making sense of the
   state-of-the-art. In Proceedings of ACL/IJCNLP, pages 656–664.
Raghunathan, K., Lee, H., Rangarajan, S., Chambers, N., Surdeanu, M.,
   Jurafsky, D., and Manning, C. (2010). A multi-pass sieve for coreference
   resolution. In Proceedings of EMNLP, pages 492–501.
Haghighi, A. and Klein, D. (2010). Coreference resolution in a modular,
   entity-centered model. In Proceedings of NAACL, pages 385–393.

perceptron-style training:
collins & roark's incremental parsing algo - 2004
goldberg/Ehhabad 2010

Goldberg, Y. and Elhadad, M. (2010). An efficient algorithm for easy-first
   non-directional dependency parsing. In Proceedings of NAACL, pages
   742–750. Association for Computational Linguistics.

4 Experiments

We implemented our algorithm in Reconcile, a research platform for
coreference resolution (Stoyanov et al., 2010b,a).

reconcile research platform [stoyanov 10] : code available

Stoyanov, V., Cardie, C., Gilbert, N., Riloff, E., Buttler, D., and Hysom,
   D. (2010b). Reconcile: A coreference resolution research
   platform. Technical report, Cornell University.


Two databases  MUC and ACE

		    MUC6 ACE04
Haghighi and Klein  81.9  76.5
Raghunathan et al.  77.7  78.1
Stoyanov et al.     88.2* 76.2

Pairwise baseline   88.2*  78.9
Easy-First_percep   88.2*  79.8
Easy-First_struct   77.5   80.1

--> improves on both in MUC, but Raghunatahn does better in ACE-4

}}



@incollection{kamp-van-11_discourse-representation-theory,
  title={Discourse representation theory},
  author={Kamp, Hans and Van Genabith, Josef and Reyle, Uwe},
  booktitle={Handbook of philosophical logic},
  editor = {Gabbay, Dov M. and Franz Guenthner},
  pages={125--394},
  year={2011},
  publisher={Springer},
  annote = {

}}

Jurafsky, Daniel; James H. Martin;
Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition
Pearson Education India, 2000, 600 pages
ISBN 9788131716724
%
% ==ch 18: Discourse - by Andrew Kehler==
% 
% --18.1 Reference Resolution--
% 
% (18.1) John went to Bill's car dealership to check out an Acura Integra. 
% 	He looked at it for about an hour.
% 
% 
% The problem of reference [is] the process by which 
% speakers use expressions like John and he in passage (18.1) to denote a
% person named John. Our discussion requires that we first define some termi-  
% nology. 
% 
% A natural language expression used to perform reference is called a REFERRING
% EXPRESSION, and the entity that is referred to is called the REFERENT.  Thus,
% John and he in passage A8.1) are referring expressions, and John is their
% referent. (To distinguish between referring expressions and their referents,
% we italicize the former.) As a convenient shorthand, we will sometimes speak
% of a referring expression referring to a referent, e.g., we might say that he
% refers to John. However, the reader should keep in mind that what we really
% mean is that the speaker is performing the act of referring to John by
% uttering he.
% 
% Two referring expressions that are used to refer to the same
% entity are said to COREFER, thus John and he corefer in passage (18.1). There
% is also a term for a referring expression that licenses the use of another,
% in the way that the mention of John allows John to be subsequently referred
% to using he. We call John the ANTECEDENT of he. Reference to an entity that
% has been previously introduced into the discourse is called ANAPHORA, and the
% referring expression used is said to be anaphoric. In passage (18.1), the
% pronouns he and it are therefore anaphoric.
% 
% Natural languages provide speakers with a variety of ways to refer to 
% entities. Say that your friend has an Acura Integra automobile and you want 
% to refer to it. Depending on the operative discourse context, you might 
% say it, this, that, this car, that car, the car, the Acura, the Integra, or my 
% friend's car, among many other possibilities. 
% 
% each type of referring expression encodes different signals about the place
% that the speaker believes the referent occupies within the hearer's set of
% beliefs. A subset of these beliefs that has a spe- cial status form the
% hearer's mental model of the ongoing discourse, which we call a discourse
% model (Webber, 1978). The discourse model contains representations of the
% entities that have been referred to in the discourse and the relationships in
% which they participate. Thus, there are two components required by a system
% to successfully produce and interpret referring expres- sions: a method for
% constructing a discourse model that evolves with the dynamically-changing
% discourse it represents, and a method for mapping be- tween the signals that
% various referring expressions encode and the hearer's set of beliefs, the
% latter of which includes this discourse model.
% 

Mitkov, Ruslan;
Anaphora resolution
Longman (Studies in Language and Linguistics), 2002, 220 pages
ISBN 0582325056, 9780582325050
% 
% ==Basic notions and terminology==
% 
% Cohesion is a phenomenon accounting for the observation (and assumption) that
% what people try to communicate in spoken or written form1 in ‘normal
% circumstances’ is a coherent whole, rather than a collection of isolated or
% unrelated sentences, phrases or words. Cohesion occurs where the
% interpretation of some element in the discourse is dependent on that of
% another and involves the use of abbreviated or alternative linguistic forms
% which can be recognised and understood by the hearer or the reader, and which
% refer to or replace previously mentioned items in the spoken or written text.
% 
% Consider the following extract from Jane Austen’s Pride and Prejudice:
% 
% 	(1.1) Elizabeth looked archly, and turned away. HER resistance had
% 		not injured her with the gentleman.
% 
% Although it is not stated explicitly, it is normal to assume that the second
% sentence is related to the first one and that her refers to Elizabeth. It is
% this reference which ensures the cohesion between the two sentences. If now
% the text is changed by replacing her with his in the second sentence or the
% whole second sentence is replaced with This book is about anaphora, cohesion
% does not occur any more: the interpretation of the second sentence in both
% cases no longer depends on the first sentence.
% 
% Discourse (1.1) features an example of anaphora with the possessive pronoun
% her referring to the previously mentioned noun phrase Elizabeth. Halliday and
% Hasan (1976) describe anaphora as ‘cohesion which points back to some
% previous item’.  The ‘pointing back’ word or phrase is called an ANAPHOR
% and the entity to which it refers or for which it stands is its
% ANTECEDENT. The process of determining the antecedent of an anaphor is called
% anaphora resolution. When the anaphor refers to an antecedent and when both
% have the same referent in the real world, they are termed
% coreferential. Consider the following example from Huddleston (1984):
% 
% (1.2) The Queen is not here yet but SHE is expected to arrive in the next half
% 	an hour.
% 
% In this example, the pronoun she is an anaphor, the Queen is its antecedent
% and she and the Queen are coreferential. Note that the antecedent is not the
% noun Queen but the noun phrase (NP) the Queen.
% 
% The relation between the anaphor and the antecedent is not to be confused
% with that between the anaphor and its referent; in the above example the
% referent the Queen is a person in the real world (e.g. Queen Elizabeth)
% whereas the antecedent the Queen is a linguistic form. Next, consider (1.3):
% 
% (1.3) [This book] is about anaphora resolution. [The book] is designed to help
% 	beginners in the field and ITS author hopes that IT will be useful.  
% 
% In this
% example there are three anaphors referring to the antecedent this book – the
% noun phrase the book, the possessive pronoun its and the personal pronoun it
% (section 1.4 below will discuss different varieties of anaphora). For all
% three anaphors, the referent in the real world is the book being read and
% therefore the anaphors and their antecedent(s) are coreferential.
% 
% On the other hand, look at this example:
% 
% 	(1.4) Stephanie balked, as did Mike.
% 
% This sentence features the verb anaphor did (see also section 1.4.4) which is a
% substitution for the antecedent balked; however, since the two terms in this
% anaphoric relation do not have a common referent, one cannot speak of coreference
% between the two.
% 
% --1.2 Coreference--
% 
% The previous section introduced examples of coreference, which is the act of
% picking out the same referent in the real world. As seen in (1.3), a specific
% anaphor and more than one of the preceding (or following) noun phrases may be
% coreferential thus forming a coreferential chain of entities which have the
% same referent. As a further illustration, in (1.5) Sophia Loren, she (from
% the first sentence), the actress, her and she (second sentence) are
% coreferential. Coreferential chains partition discourse entities into
% equivalence classes. In (1.5) the following coreferential chains can be
% singled out: {Sophia Loren, she, the actress, her, she}, {Bono, the U2
% singer}, {a thunderstorm}, {a plane}.10
% 
% (1.5) Sophia Loren says she will always be grateful to Bono. The actress
% 	revealed that the U2 singer helped her calm down when she became
% 	scared by a thunderstorm while travelling on a plane.
% 
% Definite noun phrases in copular relation are considered as coreferential, hence
% in the example
% 
% 	(1.6) David Beckham is the Manchester United midfielder.
% 
% the proper name David Beckham and the definite description the Manchester
% United midfielder are coreferential. Coreferential are also David Beckham
% and the second best player in the world in (1.7)
% 
% (1.7) David Beckham was voted the second best player in the world behind
% 	Rivaldo.
% 
% Other examples of copular relations include the relation of apposition
% illustrated by (1.8):
% 
% (1.8) Dominique Voynet, the French Environment Minister, launched a bitter
% 	attack on Mr. Prescott’s ‘chauvinism’.
% 
% In this example the definite noun phrase the French Environment Minister is
% coreferential with the NP to which it applies, in this case Dominique
% Voynet. Since proper names are regarded as definite, in the example
% 
% (1.9) Bulger is a fugitive and his sister, Jean Holland, had tried to stop
% 	the Justice Department from seizing Bulger’s winnings, one-sixth of a
% 	1991 $14.3 million jackpot.
% 
% the NP Jean Holland is coreferential with the NP to which it applies (his
% sister).  On the other hand, the indefinite predicate nominal a fugitive is
% not normally regarded as coreferential with Bulger: the fact that it is not
% specific enough means that it cannot be viewed as an NP having the same
% referent in the real world as Bulger.
% 
% It is important to point out that in some cases an NP without a
% ‘definiteness’ modifier (such as the, this, that) can still be regarded as
% specific and definite, and therefore coreferential with the NP with which it
% is in a copular relation: (1.10) Nicolas Clee, editor of the Bookseller,
% describes him as a journalist’s dream contact.
% 
% In this example editor of the Bookseller is specific enough to be regarded as
% definite, and therefore coreferential with Nicolas Clee.
% 
% Coreference is typical of anaphora realised by pronouns and non-pronominal
% definite noun phrases (see varieties of anaphora in 1.4), but does not apply
% to varieties of anaphora that are not based on referring expressions, such as
% verb anaphora. However, as was already seen with indefinite noun phrases, not
% every NP triggers coreference. Bound anaphors which have as their antecedent
% quantifying NPs such as every man, most computational linguistics, nobody,
% etc., are another example where the anaphor and the antecedent do not
% corefer. As an illustration, the relation in (1.11) is only anaphoric,
% whereas in (1.12) it is both anaphoric and coreferential.
% 
% 	(1.11) Every man has his own destiny.
% 	(1.12) John has his own destiny.
% 
% A substitution test can be used to establish coreference in (1.12) resulting
% in the semantically equivalent sentence
% 
% 	(1.13) John has John’s own destiny.
% 
% No such equivalence can be yielded with (1.11) however, where a substitution
% test produces
% 
% 	(1.14) Every man has every man’s destiny.
% 
% which is not the same statement as (1.11).
% 
% Finally, in the example
% 
% 	(1.15) The man who gave his paycheck to his wife was wiser than the
% 		man who gave it to his mistress.
% 
% the anaphor it and the antecedent paycheck do not correspond to the same
% referent in the real world but to one of a similar description (such type of
% anaphora is called identity-of-sense anaphora as opposed to
% identity-of-reference anaphora in examples (1.3) and (1.5); see also section
% 1.6 for more details). Therefore, it and his paycheck are not coreferential.
% 
% On the other hand, there may be cases where two items are coreferential without
% being anaphoric. Cross-document coreference is an obvious example: two
% mentions of the same person in two different documents will be coreferential,
% but will not stand in anaphoric relation.
% 
% Having seen some of the differences between anaphora and coreference, it is
% worth emphasising that identity-of-reference nominal anaphora23 involves
% coreference by virtue of the anaphor and its antecedent having the same
% real-world referent. Consequently, for anaphora of that type, it would be
% logical to regard each of the preceding lexical noun phrases that are
% coreferential with the anaphor(s) as a legitimate antecedent. In the light of
% this observation, the task of automatic anaphora resolution will be
% considered successful, if any of the preceding nonpronominal entities in the
% coreferential chain25 is identified as an antecedent.  Consider again
% (1.5). Here the antecedent of the anaphors she (first sentence) and the
% actress is the noun phrase Sophia Loren; both Sophia Loren and the actress
% can be considered antecedents for the anaphors her and she from the second
% sentence.
% 
% This book will focus more on the task of anaphora resolution and less on
% coreference resolution.  Whereas the task of anaphora resolution has to do
% with tracking down an antecedent of an anaphor, coreference resolution seeks
% to identify all coreference classes (chains). For more on coreference
% resolution, it is suggested that the reader consult the Message Understanding
% Conference (MUC) Proceedings in which coreference resolution is covered
% extensively (Hirschman and Chinchor 1997).
% 
% 
% --1.4.5 Zero anaphora--
% 
% Another important class of anaphora according to the form of the anaphor is
% the so-called zero anaphora or ellipsis. Zero anaphors (signalled below by ∅)
% are ‘invisible’ anaphors – at first glance they do not appear to be there
% because they are not overtly represented by a word or phrase. Since one of
% the properties and advantages of anaphora is its ability to reduce the amount
% of information to be presented via abbreviated linguistic forms, ellipsis may
% be the most sophisticated variety of anaphora.
% 
% Ellipsis is the phenomenon associated with the deletion of linguistic forms,
% thus enhancing rather than damaging the coherence of a sentence or a discourse
% segment. The resultant ‘gap’ (zero anaphor) signals the necessity of recovering
% the meaning via its antecedent.
% 
% The most common forms of ellipsis are zero pronominal anaphora, zero noun
% anaphora and verb (phrase) ellipsis.
% 
% --Zero pronominal anaphora--
% 
% Zero pronominal anaphora occurs when the anaphoric pronoun is omitted but is
% nevertheless understood. This phenomenon occurs in English in a somewhat
% restricted environment, but is so pervasive in other languages such as
% Spanish, Italian, Portuguese, Polish, Chinese, Japanese, Korean and Thai,
% that NLP applications covering these languages cannot circumvent the problem
% of zero anaphora resolution. Consider the first sentence in this paragraph.
% 
% (1.36) Zero pronominal anaphora occurs when the anaphoric pronoun is
% 	omitted but ∅ is nevertheless understood.
% 
% The third clause in this sentence features zero pronominal anaphora (the
% expected full form would have been "but it is nevertheless understood").59
% 
% Similarly the second clause of the sentence
% 
% (1.37) Willie paled and ∅ pulled the sock up quickly.60
% 
% contains a zero pronominal anaphor.
% 
% In some languages verb agreement points to a zero pronoun. As an
% illustration, consider the following example in Spanish:
% 
% (1.38) Marta está muy cansada. ∅ Ha estado trabajando todo el día.
%        Marta is very tired. (She) Has been working all day long.
% 
% Japanese, Chinese and Korean are languages with extensive use of zero
% pronouns.
% 
% 61 The following is an example of zero pronominal anaphora in Japanese.
% 
% (1.39) Nihongo o hanasu no wa kantan desu ga kaku no wa muzukashii desu.
% 	Speaking Japanese is easy but writing ∅ (= it) is difficult.
% 
% A study of anaphoric pronouns in parallel English and Japanese texts
% conducted by Uehara (1996) exemplifies the pervasive distribution of zero
% pronouns in Japanese. This study found that 14.5% of the English anaphoric
% pronouns were retained in Japanese as overt pronouns, 29% were replaced by
% overt noun phrases and 56.5% were ‘deleted’ as zero pronouns.
% 
% [I suspect that owing to richer morphological inflection, Indo-Iranian lgs
% like Hindi may also have more pro-drops. - AM]
% 

Gabbay, Dov M.; Franz Guenthner;
Handbook of Philosophical Logic: Volume 15
Springer, 2010, 416 pages
ISBN 9400704852, 9789400704855
+LOGIC PHILOSOPHY  @13nov  gabbay-guenthner-2010_HPL-2ed-v15-lambda-calculi-DRT
%
% ==Kamp Genabith Reyle: Discourse Representation Theory (DRT==
%		Hans Kamp, Josef Van Genabith, Uwe Reyle
%
% Discourse Representation Theory, or DRT, is one of a number of theories of
% dynamic semantics, which have come upon the scene in the course of the past
% twenty years. The central concern of these theories is to account for the
% context dependence of meaning. It is a ubiquitous feature of natural
% languages that utterances are interpretable only when the interpreter takes
% account of the contexts in which they are made — utterance meaning depends on
% context. Moreover, the interaction between context and utterance is
% reciprocal. Each utterance contributes (via the interpretation which it is
% given) to the context in which it is made. It modifies the context into a new
% context, in which this contribution is reflected; and it is this new context
% which then informs the interpretation of whatever utterance comes next.
% 
% The focus on context dependence has led to an important shift in
% paradigm, away from the “classical” conception of formal semantics which
% sees semantic theory as primarily concerned with reference and truth and
% towards a perspective in which the central concept is not that of truth but
% of information.
% 
% 
% --2.1 Discourse and Interpretation in Context--
% In predicate logic ([Hodges, 2001]) the following two expressions are truth
% conditionally equivalent
% 	(1) ∃xΦ ⇔ ¬∀x ¬Φ
%
% If Φ is instantiated to (delegate(x) ∧ arrive(x)), then the two formulas are
% approximate semantic representations of
% 	(2) A delegate arrived.
% and
% 	(3) It is not the case that every delegate failed to arrive.
%
% While (2) can be extended into the mini-discourse
% 	(4) A delegate^i arrived. She_i registered.
% where anaphoric relationships are indicated by subscripts for anaphors and
% corresponding superscripts for their antecedent head-words, its
% truth-conditionally
% equivalent counterpart (3) does not admit of any such extension:
%
% (5) ∗ It is not the case that every delegate i failed to arrive. Shei registered.
%
% Truth conditions alone fail to capture the contextual dimension of sentence
% interpretation. Intuitively (and pre-theoretically) the difference between
% (2) and (3) (and hence the difference between (4) and (5)) can be accounted
% for as follows: (2) updates the initially available context with an
% antecedent which can be picked up by anaphoric expressions in subsequent
% discourse; the truth conditionally equivalent (3) doesn’t.
%
% 2.2 Donkey Sentences
% Traditionally, indefinite NP’s have been translated into logic as
% predications involving existential quantification with intrasentential
% anaphors referring back to the indefinites as variables bound by the
% existential quantifiers. In many cases, this approach delivers the right
% results. However, puzzles associated with "donkey sentences" (originating
% in the middle ages and discussed in [Geach, 1962 (Third revised edition:
% 1980)]) show that indefinites cannot be translated uniformly into
% existential quantifications and demonstrate the need to revise the
% traditional quantificational bound variable approach to such NP anaphora.
%
% 	(12) If Pedroi owns a donkeyj, hei likes itj .
% 	(13) Every farmer who owns a donkeyj likes itj .
%
% It is widely agreed that (on at least one prominent reading) the truth
% conditions 
% associated with (12) and (13) correspond to (14) and (15), respectively:
%
% 	(14) ∀x[(donkey(x) ∧ own(pedro, x)) → like(pedro, x)]
% 	(15) ∀x∀y[(farmer(x) ∧ donkey(y) ∧ own(x, y)) → like(x, y)]
%
% In (14) the indefinite NP a donkey in (12) surfaces as a universally
% quantified expression taking wide scope over the material implication
% operator.  By contrast, in a sentence like (2) the indefinite a delegate
% has existential import. The occurrence of the indefinite noun phrase a
% donkey in (13) poses similar problems. The indefinite NP, this time located
% inside a relative clause modifying a universally quantified NP, surfaces as
% a universally quantified expression with wide scope in (15).
%
% Interpreting (12) under the quantifying-in approach illustrated in (6)–
% (11) results in
%
% 	(16) ∃x(donkey(x) ∧ [own(pedro, x) → like(pedro, x)])
%
% while a direct insertion approach (where quantified NPs are interpreted in
% situ [Montague, 1973]) produces an open formula, in which the x in the
% consequent of the material implication is not bound:
%
% 	(17) ∃x[donkey(x) ∧ own(pedro, x)] → like(pedro, x)
%
% Neither (16) nor (17) are adequate representations of the perceived meaning
% (14) of (12). (16) comes out true in case there is (at least) one donkey
% Pedro doesn’t own and (17) doesn’t even express a proposition.
%
% 2.3 DRT — the Basic Ingredients
%
% Examples (2), (3), (4) and (5) illustrate the need to extend the narrow
% conception of meaning as truth conditions to a more dynamic notion of
% meaning relative to context. Examples (8) and (9), (12) and (13) illustrate
% the need to reconsider the traditional quantificational and bound variable
% approach to nominal anaphora on the intra- and intersentential level.
%
% In the original formulation of DRT [Kamp, 1981a; Kamp and Reyle,
% 1993] interpretation involves a two stage process: first, the construction of
% semantic representations, referred to as Discourse Representation Structures
% (DRSs), from the input discourse and, second, a model-theoretic interpretation
% of those DRSs. The dynamic part of meaning resides in how the
% representations of new pieces of discourse are integrated into the representation
% of the already processed discourse and what effect this has on the
% integration of the representations of subsequent, further pieces of
% discourse.  Put differently, a new piece of discourse is interpreted
% against and in turn updates the representation of the already processed
% discourse and the meaning of a linguistic expression consists both in its
% update potential and its truth-conditional import in the resulting
% representation. The dynamic view of meaning in terms of updates of
% representations and the attempt at a rational reconstruction of the on-line
% and incremental character of discourse processing by human agents naturally
% leads to an algorithmic specification of DRS-construction in [Kamp, 1981a;
% Kamp and Reyle, 1993].
%
% To process a sequence of sentences S1, S2, . . . , Sn the construction
% algorithm starts with a syntactic analysis of the first sentence S1 and
% transforms it in a roughly top-down, left-to-right fashion with the help of
% DRS construction rules into a DRS K1 which serves as the context for the
% processing of the second sentence S2. The syntactic analysis of S2 is then
% added to and incrementally decomposed within the context DRS K1. Semantic
% contributions of constituent parts of S2 are integrated into DRS K1 as soon
% as they become available, eventually resulting in a complete DRS K1,2 for
% the sequence S1, S2.  Truth conditional interpretations are provided for
% completed DRSs K1, K1,2, . . . K1,...,n but not for intermediate steps
% involving application of DRS construction rules.
%
% In its original formulation, DRT tries to do justice to a conception
% prevalent in a number of AI, Cognitive Science and Linguistics approaches
% (cf. [Fodor, 1975]) according to which the human mind can be conceived of
% as an information processing device and that linguistic meanings are best
% viewed as instructions to dynamically construct and update a mental
% representation, which can then be employed in further mental processing
% (such as theoretical and practical reasoning). At the same time, complete
% meaning representations are associated with truth-conditional semantic
% interpretations. DRT’s decidedly representational stance has inspired (or
% provoked) research on a large number of "non-representational" approaches
% to dynamic semantics, cf. [Zeevat, 1989; Groenendijk and Stokhof, 1991;
% Groenendijk and Stokhof, 1990; Muskens, 1996; Eijck and Kamp, 1997; Harel,
% 1984].
%
% In the early nineties a new DRT architecture was proposed by Van Der
% Sandt and Geurts [van der Sandt, 1992; Geurts and van der Sandt, 1999;
% Geurts, 1999; Kamp, 2001a; Kamp, 2001b], based on a general treatment of
% presupposition [Soames, 1984]. Informally speaking, a (linguistic) presupposition
% is a requirement which a sentence imposes on the context in which
% it is used. If the context doesn’t satisfy the presuppositions imposed by the
% sentence, it may be be modified through "accommodation", i.e. modified or
% updated to a new context which does satisfy them.
%
% Within the new DRT architecture presuppositions are treated via a two
% stage procedure. First, a "preliminary" representation is constructed for
% each individual sentence in which all presuppositons which the sentence
% carries are given explicit representations. During the second stage the
% presuppositions represented in the preliminary representation are checked
% against the context; when necessary and feasible, the context is accommodated.
% When all presuppositions have been satisfied, the remaining nonpresuppositional
% part of the preliminary representation is merged with the
% (original or updated) context; the result is a DRS which includes both the
% context information (possibly with its accommodations) and the contribution
% made by the sentence.
%
% the contextually relevant difference
% between the truth-conditionally equivalent (2) and (3)
% are mapped into the DRSs in (24) and (25),
% respectively:
%
% (24)  	[x
% 	[delegate(x)
% 	[arrive(x)
%
% (25) ¬  [   [x		]
%      	    [delegate(x)]	⇒	¬ arrive(x)  ]
%
%
% (24) and (25) are truth-conditionally equivalent, as can be verified
% against the semantics given in Definition 10. However, (24) can be extended
% to an anaphorically resolved DRS
%
% (26)	[x y		]
% 	[delegate(x)	]
% 	[arrive(x)	]
% 	[register(y)	]
% 	[y = x		]
%
% representing the two sentence discourse (4), while (25) can only be extended
% to the unresolvable [register (y), y=?]
%
% where the remaining resolution instruction y = ? indicates that no
% antecedent for the pronoun she has been found.
%
% Mapping to DRS:
% Consider
% 	A delegate^1 arrived.  She registers
%
% The algorithm inserts the syntactic analysis of Sentence 1
% as a "reducible condition" into an empty DRS representing an
% initial empty context. A DRS construction rule for indefinite NPs matches
% the relevant part of the tree, introduces a new discourse referent x into the
% universe of the DRS under construction and adds a condition delegate(x)
% to the set of conditions.
%
% The matching part of the tree configuration is replaced by x. Next, a DRS
% construction rule for simple intransitive VP configurations applies. The
% matching tree is consumed and a condition arrive( x) is added to the DRS
% condition. This completes the processing of the first sentence of (29).
%
% In the next step the top-down construction algorithm inserts the syntactic
% analysis of the second sentence  as a reducible condition into the context
% DRS constructed from the first sentence. A DRS construction rule for
% pronominal NPs introduces a new discourse referent y into the universe of
% the DRS under construction, adds a condition y = ? to the set of conditions
% and replaces the matching part of the tree with y.
%
% Informally, y = ? can be understood as an instruction to find a suitable
% antecedent for the pronoun she. A suitable antecedent is a discourse
% referent already introduced and available in the context representation
% constructed so far. In the case at hand discourse referent x is available
% and the anaphor is resolved to y = x. Note that in this set-up the
% anaphoric NP she is resolved as soon as it is processed by the construction
% algorithm.
%
%
;;;
; ==Contents v.15==
; Lambda Calculi: A Guide 1
; 	Chris Hankin
; Interpolation and Definability 67
; 	Dov M. Gabbay and Larisa L. Maksimova
; Discourse Representation Theory 125-394
; 	Hans Kamp, Josef van Genabith and Uwe Reyle
;

Buring, Daniel [Büring];
Binding Theory
Cambridge University Press (Textbooks in Linguistics), 2005, 281 pages
ISBN 0521812801, 9780521812801
;
; Binding theory seeks to explain how different kinds of nominal expressions
; such as names, noun phrases and pronouns have anaphoric relations amongst
; one another, and how they come to have reference to things in the
; world. This textbook provides a thorough and comprehensive introduction to
; modern binding theory, introducing a huge variety of nominal and especially
; pronominal expressions from the world's languages.
