Alexander Gelbukh, Grigori Sidorov, and Igor A. Bolshakov. Dictionary-based Method for Coherence Maintenance in Man-Machine Dialogue with Indirect Antecedents and Ellipses. Proc. TSD-2000: Third International Workshop on Text, Speech and Dialogue, Brno, Czech Republic, September 13-16, 2000. Lecture Notes in Artificial Intelligence, Springer.


Dictionary-based Method for Coherence Maintenance
in Man-Machine Dialogue
with Indirect Antecedents and Ellipses

Alexander Gelbukh, Grigori Sidorov, and Igor A. Bolshakov

Natural Language Laboratory, Center for Computing Research (CIC),
National Polytechnic Institute (IPN), Av. Juan de Dios Bátiz, s/n, esq. Mendizabal,
Zacatenco, C.P. 07738, Mexico D.F., Mexico.

{gelbukh, sidorov, igor}

Abstract. Resolution of referential ambiguity is one of the most challenging problems of natural language processing. Especially frequently it is faced with in dialogues. We present a heuristic algorithm for detection of the indirect antecedents for dialogue phrases based on the use of a dictionary of prototypic scenarios associated with each headword as well as of a thesaurus of the standard type. The conditions for filtration of the candidates for the antecedent are presented. We also present a similar algorithm for reconstruction of elliptical phrases of a special kind using a combinatory dictionary.

1.                  Introduction

The problem of usual (direct) anaphora resolution in a wide range of NLP tasks – from language understanding to statistics, translation, and abstracting – have been the area of active research in recent years (Carter 1987; Cornish 1996; Fretheim and Gundel 1996; Kameyama 1997). The prevailing approaches to the resolution of direct anaphora are nowadays an integrated and an alternative ones (Mitkov 1994, 1997a, 1997b). The former approach is based on the integration of different kinds of knowledge like syntactic, semantic, and discourse, see also (Azzam et al. 1998, Ferrandez et al. 1998), while the latter uses statistical information, neural networks or the principles of reasoning with uncertainty. For ellipsis resolution there is also a tendency to use integrated information (Hahn et al. 1996). On the other hand there are attempts to exclude some types of knowledge from algorithms (Kennedy and Boguraev 1996). At the same time the idea to create methods using only dictionaries like WordNet is on the agenda (Hirst and St-Onge, 1998). In this paper we try to exploit the ideas connected with dictionary-based approaches.

Indirect anaphora has drawn less attention than the direct one. The investigation was mainly focused on its various theoretical aspects rather than on practical algorithms of resolution (Indirect Anaphora 1996, Sanford et al. 1983). For example, Erku and Gundel (1987) and Gundel et al. (1988) propose a giveness hierarchy. A proposal similar to our approach can be found in (Murata et al. 1999). The authors note that “this kind of reference (indirect anaphora) has not been thoroughly studied in natural language processing, but is important for coherence resolution, language understanding, and machine translation.” The method they suggest is based on a dictionary of examples of the form “X of Y” extracted from a text corpus. This dictionary plays the role analogous to that of our scenario dictionary, while the latter is much richer in the semantic and syntactic types of relations; also we build our dictionary on the basis of existing dictionaries of various types rather than extract it from a corpus.

Dialogue is also actively investigated, though not with relation with the anaphoric phenomena. As an example of a recent research in dialogue, see (Carberry and Lambert, 1999).

In this paper, we describe a dictionary-based method of restoration of indirect anaphoric and elliptical links in application to the dialogues where the phenomena in question are much more frequent (Sidorov and Gelbukh 1999b, Gelbukh and Sidorov 1999). In the first part of the paper, we discuss the problem of indirect anaphoric antecedents, and in the second part a modification of our algorithm to be applied to ellipsis. We formulate necessary conditions for the detection of the presence of the phenomenon (anaphora or ellipsis, correspondingly); then we discuss the structure of the dictionary used by the algorithm; finally, the algorithm is described.

For simplicity we consider the microdialogues, containing the phrase of one of the participants (the computer) and the answer of the other (the human), like

1.        There is a PS/2 computer in the store.
Is the motherboard from Intel?

2.        There is a printer in the store.”

3.        The user found an error in the program.
Really fatal?

Example 1 demonstrates the case of the indirect anaphora. The problem is in the interpretation of the definite clause in the second utterance of each dialogue. Usually, the definite clause refers to some entity already introduced in the common view of the participants of the communication (in this case implicitly).  Examples 2 and 3 illustrate the cases of the elliptical construction where the omitted word was introduced into the dialogue in the previous utterance(s), and the purpose of the utterance is to clarify or add a detail to a previous one. Thus, we leave aside the cases where the omitted word was introduced, say, by the extralinguistic situation (say, both participants see a strange man; “Drunk,” says one of them) or by the pragmatics of the communication.

2.                  Indirect Antecedents in Dialogue

Let us call the entities implicitly or potentially introduced by a word a prototypic scenario of this word. For example, the scenario for the word house includes kitchen, roof, windows, district, place, owner, etc. We suggest that the indirect anaphoric relation holds between a word and an element of the prototypic scenario of another word previously appeared in the text; such an implicitly introduced element does not have its own surface representation in the text.

We call the words that are equivalent for our algorithms compatible. Thus, synonyms, derivatives, hypernyms, hyponyms, and metaphors of a word can be compatible with it.

Let us denote the potential antecedent x, the anaphor y, and the scenario of a word w, S (w). Denoting the compatibility relation between the words w and u as w ~ u, we can formulate the following condition:

Condition 1. Indirect anaphoric relation between x and y is possible only if any of the following conditions holds:

$ w Î S (x) such that y ~ w, or

$ w Î S (y) such that x ~ w, or

$ w Î S (x) and $ u Î S (y) such that u ~ w.

The algorithm for detecting anaphoric links between the utterances in the dialogue works as follows. It analyzes the current utterance word by word. If a word is introduced with a definite article or a demonstrative pronoun, then it is a potential anaphor, and the algorithm tries to find a plausible antecedent for it. It looks for the possible candidates first within the same utterance and then in the previous utterance(s); from right to left. The candidates are a priori scored the lower the greater the distance; the algorithm stops when an antecedent with high score is found or the scores become too little.

Namely, let distance be the distance between the possible antecedent and the potential anaphor. In the current version of the algorithm, we use the linear distance. In a more sophisticated version, the syntactic distance (the number of constituents between the two words) or some combination of the two measures can be used.

Let probability-of-variantn be the a priori probability associated with the corresponding parts 1, 2, and 3 of Condition 1. This probability is a statistical characteristic of the given language (or sublanguage) that characterizes the frequency of the use of the corresponding constructions.

Let threshold be the maximum allowed value which depends on distance and probability-of-variantn. It is used to determine the stop condition of algorithm.

Determining the exact values of these parameters goes beyond the scope of this paper; in our experiments, we used the following approximate values found empirically: threshold = 0.05, probability-of-variant1 = 1, probability-of-variant2 = 0.75, and probability-of-variant3 = 0.4.


repeat for each definite noun x of the utterance, from left to right

repeat for each word y, from right to left, from the last word of the previous utterance

distance = the distance between x and y

if $ n such that part n of Condition 1 holds for x and y then

the variant y of the antecedent for x is temporarily stored,

its weight w = probability-of-variantn ´ (1 / distance)

if  (max {probability-of-varianti} ´ (1 / distance) > threshold) then

                break the loop and go to next x

else pass to the next word y to the left


The variant y (if any) with the best weight w is chosen and sent to the output.



Figure 1. The algorithm for finding the antecedent(s).

To check the possibility of an indirect antecedent, a prototypic scenario dictionary is used. In our experiments, we used a dictionary compiled from several sources, Currently the dictionary contains about 1,000 entries, and the scenarios contain in total about 40,000 words.

3.                  Ellipsis in Dialogue

For the purposes of the coherence maintenance in the dialogue with ellipsis it is appropriate to view the interpretation as establishing a link (similar to a syntactic one) between the corresponding words across the utterances. For instance, in example 3 the link holds between the words fatal and error:

Let us call such a link elliptical relation, the word fatal being the source of the relation and the word error being its target.

To restore the elliptical relations in the dialogue, we use the same idea as for the indirect anaphoric relations. Namely, we try to find a highly probable target for such a relation and in this way detect the very presence of the relation.

We call the words that potentially, with high probability can form a word combination combinable, and denote this as  w. Here u is the governor and w is the dependent: error  fatal is true since the syntactic combination fatal ¬ error is highly probable; however, fatal  error is not true since fatal ® error is impossible. Also program  fatal is not true since normally fatal program is not a combination that can be normally expected in the text. Note that this is a lexical property of the corresponding words, specified in the dictionary, rather than the syntactic relation in a specific context.

Condition 2. Elliptical relation between x and y is possible only if x  y.

If the lexical information is not available for a specific word, a relaxed condition using only the parts of speech POS (x) and POS (y) can be used, with less reliability of the positive result:

Condition 3. Elliptical relation between x and y is possible only if POS (x POS (y).

Since we treat the type of the ellipsis under consideration as a potential insertion of a word into the target utterance, the Condition 3 can be generalized as follows:

Condition 4. Elliptical relation between x and y is possible only if the utterance containing the target (x) remains syntactically correct when the source (y) is added to it and syntactically connected to x.

However, such a generalized condition is much more expensive computationally to test.

The algorithm for detecting potential antecedents is very much similar to the one for detecting the indirect antecedents. Namely, for each case of structural incompleteness, the hypothesis of the possible elliptical link is checked by trying, word by word, the candidates in the previous utterance and verifying the Conditions 2 to 4. Since the condition 3 is less computationally expensive, it is tried first; then the condition 2. The process stops when a plausible antecedent is found or when the distance from the end of the previous utterance becomes too large.

To check the Condition 2, a dictionary of word combinations is used. For each headword, the dictionary lists the words that can syntactically combine with it, together with the prepositions (or grammatical cases) used in such a combination. In addition to the mere fact of combinability, the dictionary can specify a quantitative measure of the probability (frequency) of the combination in the texts.

In our experiments, we use the CrossLexica dictionary (Bolshakov and Gelbukh, 2000) in its part of word combinations. The Russian version of this dictionary contains more than 500,000 word combinations; the English version is under construction.

As in the case of the indirect anaphora, some lexical relations – such as synonyms, hyponyms, etc. – are transparent for syntactical combinability.

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