We present an algorithm for anaphora resolutkm which is a modified and extended version of that developed by (Lappin and Leass,/994). In contrast to that work, our algorithm does not require in-depth, full, syn.. tactic parsing of text. Instead, with minimal compromise in output quality, the modifications enable the resolution process to work from tile output of a part of speech tagge~; enriched only with annotations of grammatica] functkm of lexical items in the input text stream. Evaluation of the results of our in-tplementation demonstrates that accurate anaphora resolution can be realized within natural language processing fl'ameworks which do not--~,)r cannot-employ robust and rcqiable parsing components. (l,appin and Leass, 1994) describe an algorithm for pronominal anaphora resolution with high rate of correct analyses. While one of the strong points of this algorithm is that it operates primarily on syntactic information ahme, this also turns out to be a limiting factor for its wide use: current state-of-the-art of practically applicable parsing technology still falls short of robust and reliable delivery of syntactic analysis of real texts to the level of detail and precision that the filters a nd constraints described by I ,appin and l ,eass assume. We are particularly interested in a class of text processing applications, capable of delivery of content analysis to a depth inw~lving non-trivial amount of discourse processing, including anaphora resolution. The operational context prohibits us from making any assumptions concerning domain, style, and genre of input; as a result, we have developed a text processing framework which builds its capabilities entirely on the basis of a considerably shallower linguistic analysis of the input stream, thus trading off depth of base level analysis for breadth of cown:age. In this paper, we present work on modifying the lmppin/Leass algorithm in a way which enables it to work off a flat morpho-syntactic analysis of the sentences of a text, while retaining a degree of quality and accuracy in pronorainal anaphora resolution comparable to that reported in (Lappin and l,eass, 1994). The modifications discussed below make the algorithm available to a wide range of text processing frameworks, which, due to the lack of full syntactic parsing capability, norreally would have been unable to use this high precision anap hora resolution tool. The work is additionally important, we feel, as it shows that informatkm about the content and logical structure of a text, in princi-. pie a core requirement for higher level semantic and discourse processes, can be effectively approximated by the right mix of constituent analysis and inferences about functional relations. The base level linguistic analysis for actaphora resolution is the output of a part of speech tagger, augmented with syntactic function annotatkms for each input to. ken; this kind of analysis is generated by the morpbosyntactic tagging system described in (Voutilainen et al., 1992) , (Karlsson et al., 1995) (hencehvth 1,1NC:-~;olq'). In addition to extremely high levels of accuracy in recall and precision of tag assignment ((VoutiJainen et al., 1992) report 99.77°/,, overall recall and 95.54% overall preciskm, over a variety of text genres, and in comparison with other state-of-the-art tagging systems), the primary motivation for adopting this system is the requirement to develop a robust text processor--with anaphora resolution being just one of its discourse analysis functkms capable of reliably handling arbitrary kinds of input. The tagger provides a very simple analysis of the structure of the text: for each lexical item in each sentence, it provides a set of values which indicate the morphological, lexical, grammatical and syntactic features of the item in tile context in which it appears. In addition, the modified algorithm we present requh:es annota tion of the input text stream by a simple position--identification function which associates an integer with each token in a text sequentially (we will refer to a token's integer value as its oJ~et). As an example, given the text "For 1995 the company set up its headquarters in Hall ] l, the newest and most presti-. gious of CeBIT's 23 hal Is." tile anaphora resolutkm algorithm would be presented with the h}llowing analysis stream. Note, in particu-. lar, the grammatical function information (e.g., @SUl~J, O)q.FMAINV) and the integer values (e.g., "offt 39") associa ted with each token.
REF use gender information produced by their enhanced part-of-speech tagger.
Anaphora For Everyone: Pronominal Anaphora Resolution Without A Parser
International Conference On Computational Linguistics
Computer Science