NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition

Abstract

Background: Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing.

Banner: An Executable Survey of Advances in Biomedical Named Entity Recognition

“…While many systems use some form of stemming, BANNER instead employs lemmatization [16], which is similar in purpose except that words are converted into their base form instead of simply removing the suffix. Also notable is the numeric normalization feature [15], which replaces the digits in each token with a representative digit (e.g. "0").…”

Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

“…For example, the adjective "human" appears in at the beginning of 657 cell types, 213 proteins and 354 DNA entities, but was missed in other 96 equivalent cases: 31 for cell types, 29 for proteins and 25 for DNAs. A description of other inconsistences and annotation problems detected in the training set can be found in [47] and [45].…”

Using contextual information to clarify Gene Normalization ambiguity

“…We also apply suspending hyphen rules to expanded series such as "GlcNAc6ST-1, -2, and -3" to "GlcNAc6ST-1, GlcNAc6ST-2 and GlcNAc6ST-3". Next, our NERBio system [4] identifies all gene mentions in the given text. Finally, four post-processing rules are employed to identify more gene mentions.…”