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<Paper uid="W96-0207">
  <Title>i Combining Hand-crafted Rules and Unsupervised Learning in Constraint-based Morphological Disambiguation</Title>
  <Section position="1" start_page="0" end_page="69" type="abstr">
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Abstract 1 Introduction
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    <Paragraph position="0"> This paper presents a constraint-based morphological disambiguation approach that is applicable languages with complex morphology-specifically agglutinative languages with productive inflectional and derivational morphological phenomena. In certain respects, our approach has been motivated by Brill's recent work (Brill, 1995b), but with the observation that his transformational approach is not directly applicable to languages like Turkish. Our system combines corpus independent hand-crafted constraint rules, constraint rules that are learned via unsupervised learning from a training corpus, and additional statistical information from the corpus to be morphologically disambiguated. The hand-crafted rules are linguistically motivated and tuned to improve precision without sacrificing recall. The unsupervised learning process produces two sets of rules: (i) choose rules which choose morphological parses of a lexical item satisfying constraint effectively discarding other parses, and (ii) delete rules, which delete parses satisfying a constraint. Our approach also uses a novel approach to unknown word processing by employing a secondary morphological processor which recovers any relevant inflectional and derivational information from a lexieal item whose root is unknown. With this approach, well below 1% of the tokens remains as unknown in the texts we have experimented with. Our results indicate that by combining these hand-crafted, statistical and learned information sources, we can attain a recall of 96 to 97% with a corresponding precision of 93 to 94%, and ambiguity of 1.02 to 1.03 parses per token.</Paragraph>
    <Paragraph position="1"> Automatic morphological disambiguation is a very crucial component in higher level analysis of natural language text corpora. Morphological disambiguation facilitates parsing, essentially by performing a certain amount of ambiguity resolution using relatively cheaper methods (e.g., Gfing6rdii and Oflazer (1995)). There has been a large number of studies in tagging and morphological disambiguation using various techniques. Part-of-speech tagging systems have used either a statistical approach where a large corpora has been used to train a probabilistic model which then has been used to tag new text, assigning the most likely tag for a given word in a given context (e.g., Church (1988), Cutting et al. (1992), DeRose (1988)). Another approach is the rule-based or constraint-based approach, recently most prominently exemplified by the Constraint Grammar work (Karlsson et al., 1995; Voutilainen, 1995b; Voutilainen et al., 1992; Voutilainen and Tapanainen, 1993), where a large number of hand-crafted linguistic constraints are used to eliminate impossible tags or morphological parses for a given word in a given context. Brill (1992; 1994; 1995a) has presented a transformation-based learning approach, which induces rules from tagged corpora. Recently he has extended this work so that learning can proceed in an unsupervised manner using an untagged corpus (Brill, 1995b). Levinger et al. (1995) have recently reported on an approach that learns morpho-lexical probabilities from untagged corpus and have the used the resulting information in morphological disambiguation in Hebrew.</Paragraph>
    <Paragraph position="2"> In contrast to languages like English, for which there is a very small number of possible word forms with a given root word, and a small number of tags associated with a given lexical form, languages like Turkish or Finnish with very productive agglutinative morphology where it is possible to produce thousands of forms (or even millions (Hankamer, 1989)) for a given root word, pose a challenging problem for morphological disambiguation. In English, for example, a word such as make or set can be verb  or a noun. In Turkish, even though there are ambiguities of such sort, the agglutinative nature of the language usually helps resolution of such ambiguities due to restrictions on lnorphotactics. On the other hand, this very nature introduces another kind of ambiguity, where a lexical form can be morphologically interpreted in many ways, some with totally unrelated roots and morphological features, as will be exemplified in the next section.</Paragraph>
    <Paragraph position="3"> Our previous approach to tagging and morphological disambiguation for Turkish text had employed a constraint-based approach (Oflazer and Kuru6z, 1994) along the general lines of similar previous work for English (Karlsson et al., 1995; Voutilainen et al., 1992; Voutilainen and Tapanainen, 1993). Although the results obtained there were reasonable, the fact that all constraint rules were hand crafted, posed a rather serious impediment to the generality and improvement of the system.</Paragraph>
    <Paragraph position="4"> In this paper we present a constraint-based morphological disambiguation approach that uses unsupervised learning component to discover some of the constraints it uses in conjunction with hand-crafted rules. It is specifically applicable to languages with productive inflectional and derivational morphological processes, such as Turkish, where morphological ambiguity has a rather different nature than that found in languages like English. Our approach starts with a set of corpus-independent hand-crafted rules that reduce morphological ambiguity (hence improve precision) without sacrificing recall. It then uses an untagged training corpus in which all lexical items have been annotated with all possible morphological analyses, incrementally proposing and evaluating additional (possibly corpus dependent) constraints for disambiguation of morphological parses using the constraints imposed by unambiguous contexts. These rules choose or delete parses with specified features. In certain respects, our approach has been motivated by Brill's recent work (Brill, 1995b), but with the observation that his transformational approach is not directly applicable to languages like Turkish, where tags associated with forms are not predictable in advance.</Paragraph>
    <Paragraph position="5"> In the following sections, we present an overview of the morphological disambiguation problem, highlighted with examples from Turkish. We then present the details of our approach and results. We finally conclude after a discussion and evaluation of our results.</Paragraph>
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