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- W4293551639 abstract "We consider two models for the sequence labeling (tagging) problem. The first one is a {em Pattern-Based Conditional Random Field }(PB), in which the energy of a string (chain labeling) $x=x_1ldots x_nin D^n$ is a sum of terms over intervals $[i,j]$ where each term is non-zero only if the substring $x_ildots x_j$ equals a prespecified word $win Lambda$. The second model is a {em Weighted Context-Free Grammar }(WCFG) frequently used for natural language processing. PB and WCFG encode local and non-local interactions respectively, and thus can be viewed as complementary. We propose a {em Grammatical Pattern-Based CRF model }(GPB) that combines the two in a natural way. We argue that it has certain advantages over existing approaches such as the {em Hybrid model} of Bened{'i} and Sanchez that combines {em $mbox{$N$-grams}$} and WCFGs. The focus of this paper is to analyze the complexity of inference tasks in a GPB such as computing MAP. We present a polynomial-time algorithm for general GPBs and a faster version for a special case that we call {em Interaction Grammars}." @default.
- W4293551639 created "2022-08-30" @default.
- W4293551639 creator A5021390142 @default.
- W4293551639 creator A5029285634 @default.
- W4293551639 date "2014-04-22" @default.
- W4293551639 modified "2023-09-23" @default.
- W4293551639 title "Combining pattern-based CRFs and weighted context-free grammars" @default.
- W4293551639 doi "https://doi.org/10.48550/arxiv.1404.5475" @default.
- W4293551639 hasPublicationYear "2014" @default.
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