Extracting information from unstructured text data is a compute-intensive task. The performance of general-purpose processors cannot keep up with the rapid growth of textual data. Therefore we discuss the use of FPGAs to perform large scale text analytics. We present a framework consisting of a compiler and an operator library capable of generating a Verilog processing pipeline from a text analytics query specified in the annotation query language AQL. The operator library comprises a set of configurable modules capable of performing relational and extraction tasks which can be assembled by the compiler to represent a full annotation operator graph. Leveraging the nature of text processing we show that most tasks can be performed in an efficient streaming fashion. We evaluate the performance, power consumption and hardware utilization of our approach for a set of different queries compiled to a Stratix IV FPGA. Measurements show an up to 79 times improvement of document-throughput over a 64 threaded software implementation on a POWER7 server. Moreover the accelerated system's energy efficiency is up to 85 times better.