Language use in context Janyce Wieb e Graeme Hirst and Diane Horton THIS IS NOT THE FINAL VERSION Authors addresses Wieb e Department of Computer Science and the Computing Research Lab oratory New Mexico State University Box Dept CS Las Cruces NM email wieb ecsnmsuedu phone fax Hirst Department of Computer Science University of Toronto Toronto Ontario Canada fax MS A email ghcstorontoedu phone Department of Computer Science University of Toronto Toronto Ontario Canada Horton MS A email dianehcstorontoedu phone fax Despite the new emphasis on applicationdriven research in computational linguistics much fundamental research remains to b e done In this pap er we describ e recent computational work investigating language use in context This means rst going b eyond sentence b ound oursetreating texts or dialogues as wholes comp osed of interrelated aries and pro cessing disc parts rather than merely as sequences of isolated sentences Any piece of discourse establishes a linguistic context against which subsequent utterances must b e understo o d Second b eyond articipatory context A sp eaker or writer directs an utterance or the linguistic context is the p text toward a hearer or reader and do es so with a particular intent or purp oseto inform to amuse to collab orate in a task p erhaps The form and content of the utterance are chosen accordingly and the listener or reader must infer this intent as part of understanding We will discuss the comprehension and pro duction of language lo oking at b oth texts and dialogues A text to b e pro cessed might b e for example a newspap er or magazine article o d or that is b eing translated into another language or whose content is to b e understo gue to b e pro cessed might abstracted in an information storage and retrieval system A dialo b e a conversation sp oken or typ ed b etween a human and a computer in service of some collab orative task Many of the problems that we shall describ e b elow o ccur in b oth kinds of discourse We will use the terms speaker and writer almost interchangeably and similarly he arer and reader The underlying goal of the research describ ed in this sp ecial issue is to move b eyond toy systems and come to grips with real language While the research describ ed in the other pap ers in this issue fo cuses on robustly pro cessing massive amounts of text the work describ ed here fo cuses on understanding in computational terms the complexities and subtleties of language as p eople really use it In a pap er of this length we cannot hop e to describ e all of the recent imp ortant work addressing language use in context For example we will not discuss work on pronoun reso lution ellipsis metaphor or many asp ects of b elief ascription STR UCTURE BEYOND THE SENTENCE BOUNDARY Discourse segmentation Discourse has a rich structure Sentences group together and there are a variety of ways in which they might b e related to one another Understanding what a discourse means requires determining how the various pieces t together Consider the following excerpt from the intro duction to a textb o ok on programming in C { One of the central goals of this text is to enable teachers to manage Cs inherent complexity Managing complexity however is precisely what we do as programmers When we are faced with a problem that is to o complex for immediate solution we divide it into smaller pieces and consider each one indep endently Moreover when the complexity of one of those pieces crosses a certain threshold it makes sense to isolate that complexity by dening a separate abstraction that has a simple interface The interface protects clients from the underlying details of the abstraction thereby simplifying the conceptual structure { The same approach works for teaching programming To make the material easier for students to learn this text adopts a librarybased 1 approach that emphasizes the principle of abstraction When a p erson reads this excerpt he or she gets more from it than just the meanings of e relationships among the individual sentences Understanding the rhetorical or coherenc pieces of the text is an imp ortant asp ect of understanding the text as a whole For example sentences give sp ecic details that expand on what is said in sentence Moreover text has relationships at many levels for example not only are sentences related to sentence but they have relationships among themselves as well Fortunately there are sometimes cue phrases or textual markings that help the reader gure out the relationships in sentence for and in sp oken dialogue intonation can help to o The word however instance signals some sort of contrast with what was said in Readers have to recover the structure of a discourse not only so that they can infer the rela tionships among the pieces but also b ecause the structure constrains other essential asp ects of understanding such as guring out what a pronoun refers to Consider this conversation A Sheila wants you to call her ab out the bicycle Has she found a ro ommate yet B A Yeah Her old friend Linda is moving here from Waterlo o to start a new job She moves in next week Anyway you should phone her to day The pronoun her in sentence clearly refers to Sheila even though it was Linda who was just referred to by she in the previous sentence The structure of the dialogue can help the reader to identify the correct referent Sentences interrupt what was under discussion in but sentence returns to the topic of Notice that the cue phrase anyway p ossibly accompanied by a small pause or change in pitch patterns gives a strong hint of this structure The structure constrains the p ossible referents for the pronoun her in the referent is more likely to come from sentence than from In fact the p erception of the structure of the discourse and the interpretation of pronouns constrain one another In addition to relationships concerning the content of the text there are relationships concerning the writers or sp eakers intentions and the two are closely linked In example the author had a reason for writing sentences p erhaps to clarify for the reader what was written in The details given in serve this purp ose of the author There is evidence that p eople do p erform this kind of segmentation of discourse during understanding For example P assonneau and Litman and Hirschb erg and Grosz found 1 Eric S Rob erts The Art and Science of C A LibraryBased Introduction to Computer Science Addison Wesley page xv statistically signicant agreement among sub jects who were asked to p erform a discourse segmentation task How might a computer p erform such segmentation or pro duce language from which such segments can b e recovered In their study Hirschb erg and Grosz also investigated the relationship b etween features of intonation such as pitch range and timing and the structure of the discourse They found that at b oth the lo cal and global levels of discourse there are statistically signicant correlations b etween certain features of discourse structure and certain intonational features Another imp ortant kind of indication is cue phrases such as anyway however wel l stil l example and now which often provide explicit information ab out the structure of a for discourse For example now can b e used to intro duce a new subtopic But many words that serve as cue phrases also have other usesfor example now can simply mean at this time To see how these ambiguities could b e resolved Hirschb erg and Litman studied cuephrase usage in sp eech and develop ed a metho d for disambiguating cue phrases by means of intonational features of sp eech They also discovered textual features of transcrib ed sp eech such as punctuation that are relatively easy to extract from transcriptions and can b e used as additional aids in cuephrase disambiguation Much computational work in discourse pro cessing has assumed that cuephrase disambiguation is feasible Hirschb erg and Litmans ndings provide empirical supp ort for this assumption The research describ ed ab ove has obvious applications in b oth sp eech generation and sp eech understanding Sp eech generation systems can use intonation and cue phrases as p eo ple do to help to break the discourse into appropriate segments thereby assisting the listener in accomplishing other tasks that are crucial to understanding sp eech such as determining the referents of noun phrases and recognizing the rhetorical and intentional relationships b etween segments And sp eech recognition systems can use intonational features and cue phrases and p erhaps also textual features if the sp eech has b een transcrib ed to help p erform segmentation and infer relationships b etween segments In fact ATT Bell Lab oratories TexttoSp eech System based on Hirschb erg and Litmans work do es b oth it disambiguates cue phrases in text on the basis of textual features and then generates cue phrases in such a way that the intonational features suggest how the cue phrases are b eing used Relationships within discourse segments We now turn to some recent work that investigates the structure within discourse segments Hobbs et al and others have suggested that during understanding p eople make de feasible assumptions that is assumptions that are consistent with what they b elieve but which can b e later overridden by contrary evidence Such assumptions lead them to a plau y and Jensen apply this approach sible coherent interpretation of the discourse Zadrozn holistically to paragraphs with the goal of nding interpretations of individual