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String similarity is most often measured by weighted or unweighted edit distance d(x, y). Ristad and Yianilos (1998) defined stochastic edit distance—a probability distribution p(y | x) whose parameters can be trained from data. We generalize this so that the probability of choosing each edit operation can depend on contex-tual features. We show how to(More)
We present LEMMING, a modular log-linear model that jointly models lemmati-zation and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or an-alyzers. LEMMING sets the new state of the art in token-based statistical(More)
How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the output string, using recurrent neural networks. In contrast, we propose to keep the(More)
We present labeled morphological segmentation—an alternative view of morphological processing that unifies several tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmen-tation system that, contrary to previous work, explicitly models morphotactics. We show improved performance on three tasks for all(More)
This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic with data obtained from both online newspaper commentary and Twitter. Most Arabic corpora are small and focus on Modern Standard Arabic (MSA). There has been recent interest, however, in the construction of dialectal Arabic corpora (Zaidan and Callison-Burch, 2011a;(More)
The observed pronunciations or spellings of words are often explained as arising from the " underlying forms " of their morphemes. These forms are latent strings that linguists try to reconstruct by hand. We propose to reconstruct them automatically at scale, enabling generalization to new words. Given some surface word types of a concatenative language(More)
We present a model of morphological seg-mentation that jointly learns to segment and restore orthographic changes, e.g., funniest → fun-y-est. We term this form of analysis canon-ical segmentation and contrast it with the traditional surface segmentation, which segments a surface form into a sequence of substrings, e.g., funniest → funn-i-est. We derive an(More)
We present penalized expectation propagation (PEP), a novel algorithm for approximate inference in graphical models. Expectation propagation is a variant of loopy belief propagation that keeps messages tractable by projecting them back into a given family of functions. Our extension, PEP, uses a structured-sparsity penalty to encourage simple messages ,(More)
We investigate dual decomposition for joint MAP inference of many strings. Given an arbitrary graphical model, we decompose it into small acyclic sub-models, whose MAP configurations can be found by finite-state composition and dynamic programming. We force the solutions of these subproblems to agree on overlapping variables, by tuning Lagrange multi-pliers(More)