Online content analysis employs algorithmic methods to identify entities in unstructured text. Both machine learning and knowledge-base approaches lie at the foundation of contemporary named entities extraction systems. However, the progress in deploying these approaches on web-scale has been been hampered by the computational cost of NLP over massive text corpora. We present SpeedRead (SR), a named entity recognition pipeline that runs at least 10 times faster than Stanford NLP pipeline. This pipeline consists of a high performance Penn Treebank- compliant tokenizer, close to state-of-art part-of-speech (POS) tagger and knowledge-based named entity recognizer.
Stanford NLP pipeline @cite_2 @cite_18 @cite_6 @cite_10 @cite_7 is one of the most popular and used NLP packages. The pipeline is rich in features, flexible for tweaking and supports many natural languages. Despite being written in Java, there are many other programming language bindings that are maintained by the community. The pipeline offers a tokenization, POS tagging, named entity recognition, parsing and co-referencing resolution. The pipeline requirements of memory and computation are non-trivial. To accommodate the various computational resources, the pipeline offers several models for each task that vary in speed, memory consumption and accuracy. In general, to achieve good performance in terms of speed, the user has to increase the memory available to the pipeline to 1-3 GiBs and choose the faster but less accurate models.
@cite_18 @cite_7 @cite_6 @cite_2 @cite_10
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We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. Using these ideas together, the resulting tagger gives a 97.24 accuracy on the Penn Treebank WSJ, an error reduction of 4.4 on the best previous single automatically learned tagging result. This paper details the coreference resolution system submitted by Stanford at the CoNLL-2011 shared task. Our system is a collection of deterministic coreference resolution models that incorporate lexical, syntactic, semantic, and discourse information. All these models use global document-level information by sharing mention attributes, such as gender and number, across mentions in the same cluster. We participated in both the open and closed tracks and submitted results using both predicted and gold mentions. Our system was ranked first in both tracks, with a score of 57.8 in the closed track and 58.3 in the open track. We discuss two named-entity recognition models which use characters and character n-grams either exclusively or as an important part of their data representation. The first model is a character-level HMM with minimal context information, and the second model is a maximum-entropy conditional markov model with substantially richer context features. Our best model achieves an overall F1 of 86.07 on the English test data (92.31 on the development data). This number represents a 25 error reduction over the same model without word-internal (substring) features. This paper presents results for a maximum-entropy-based part of speech tagger, which achieves superior performance principally by enriching the information sources used for tagging. In particular, we get improved results by incorporating these features: (i) more extensive treatment of capitalization for unknown words; (ii) features for the disambiguation of the tense forms of verbs; (iii) features for disambiguating particles from prepositions and adverbs. The best resulting accuracy for the tagger on the Penn Treebank is 96.86 overall, and 86.91 on previously unseen words. Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9 over state-of-the-art systems on two established information extraction tasks.