Morphological Segmentation Inside-Out

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2325–2330 Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics Morphological Segmentation Inside-Out Ryan Cotterell@ Arun KumarG Hinrich Sch¨utzeH @Department of Computer Science, Johns Hopkins University GFaculty of Arts and Humanities, Universitat Oberta de Catalunya HCIS, LMU Munich {ryan.cotterell}@jhu.edu Abstract Morphological segmentation has traditionally been modeled with non-hierarchical models, which yield flat segmentations as output. In many cases, however, proper morphologi- cal analysis requires hierarchical structure— especially in the case of derivational morphol- ogy. In this work, we introduce a discrimina- tive, joint model of morphological segmenta- tion along with the orthographic changes that occur during word formation. To the best of our knowledge, this is the first attempt to approach discriminative segmentation with a context-free model. Additionally, we release an annotated treebank of 7454 English words with constituency parses, encouraging future research in this area.1 1 Introduction In NLP, supervised morphological segmentation has typically been viewed as either a sequence- labeling or a segmentation task (Ruokolainen et al., 2016). In contrast, we consider a hierarchical ap- proach, employing a context-free grammar (CFG). CFGs provide a richer model of morphology: They capture (i) the intuition that words themselves have internal constituents, which belong to different cat- egories, as well as (ii) the order in which affixes are attached. Moreover, many morphological pro- cesses, e.g., compounding and reduplication, are best modeled as hierarchical; thus, context-free models are expressively more appropriate. The purpose of morphological segmentation is to decompose words into smaller units, known as morphemes, which are typically taken to be the smallest meaning-bearing units in language. This work concerns itself with modeling hierarchical structure over these morphemes. Note a simple flat 1We found post publication that CELEX (Baayen et al., 1993) has annotated words for hierarchical morphological segmentation as well. morphological segmentation can also be straightfor- wardly derived from the CFG parse tree. Segmenta- tions have found use in a diverse set of NLP appli- cations, e.g., automatic speech recognition (Afify et al., 2006), keyword spotting (Narasimhan et al., 2014), machine translation (Clifton and Sarkar, 2011) and parsing (Seeker and C¸ etino˘glu, 2015). In contrast to prior work, we focus on canoni- cal segmentation, i.e., we seek to jointly model orthographic changes and segmentation. For in- stance, the canonical segmentation of untestably is un+test+able+ly, where we map ably to able+ly, restoring the letters le. We make two contributions: (i) We introduce a joint model for canonical segmentation with a CFG backbone. We experimentally show that this model outperforms a semi-Markov model on flat segmentation. (ii) We release the first morphology treebank, consisting of 7454 English word types, each annotated with a full constituency parse. 2 The Case For Hierarchical Structure Why should we analyze morphology hierarchi- cally? It is true that we can model much of mor- phology with finite-state machinery (Beesley and Karttunen, 2003), but there are, nevertheless, many cases where hierarchical structure appears requi- site. For instance, the flat segmentation of the word untestably�→un+test+able+ly …

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