Named entity recognition without domain-knowledge using conditional random fields

This paper addresses the problem of not using any domain-knowledge in named entity recognition (NER) tasks. Experiments on two well-known datasets show that the currently mostly used technique – conditional random fields (CRF) – achieves results which are respectable. It is discussed if it is acceptable to pass on better results to get results in a faster and modular way. 1. Conditional Random Fields CRFs are undirected graphical models (Lafferty et al., 2001). In theory of CRFs a set of states Y is globally conditioned by an observation sequence X. In the case of NER the states are the alphabet used for tagging text sequences. The text sequences on the other hand are the so called observation sequences. The probability of a state sequence being the best for a given observation sequence is calculated by potential functions. There are two different kinds of potential functions: state-features and transition-features. State-features are used to examine states and corresponding observations at one position of the sequence. An exemplary state-feature is: ”The observation Hamburg leads to the state ’Location’” Transition-features are used to analyse transitions between states and the observations that lead to such transitions. An exemplary transition-feature is: ”The transition from null to Person is done if the first word is ’Mr.’ and the second word is any other word”. 2. Implementation for Yale The implementation to be described is based on an existing java-implementation of CRFs (Sarawagi & Cohen, 2004) and extends it for universality. Finally the implementation was integrated into the learning environment Yale (Mierswa et al., 2006) as a plugin for textmining. In many textmining-environments the ”bag-of-words”-representation is the mostly used technique to examine texts (for classification for instance). That situation also was existent in Yale. But for NER it is necessary to conserve the sequence-character of texts – in contrast to a ”bag of word”-solution which ignores such characteristics. Therefore the author designed an environment to access and augment texts in Yale. In addition to the possibility to store texts in a sequential way, some basic features of texts were defined to extract them out of the text for using them as features in the CRF. Some of these features are for example ngrams, prefixes, suffixes and so on. The expectation is that such features can be used for every kind of text so no explicit domain-knowledge is needed. A user has the possibility to handle textanalysis-tasks in a modular way: load texts into Yale, enrich them by additional features like all ngrams of the text and finally use a learner for texts – in this case CRF. Such modularity can easily be extended by new feature-extraction-operators for example. But the implementation – in its current state – is just a first step. The goal is to develop a more modular text-analysisenvironment which can – by using machine learning techniques – determine the best selection of features by its own. 3. NER without domain-knowledge In past work (Jungermann, 2006) the NER-tasks of the JNLPBA2004 and of the CoNLL2003 were analyzed. Different features like n-grams, prefixes, suffixes, regular expressions and so on were used. The results are shown in Table 1. In addition to the results of the author the best results – achieved by the conferenceparticipants – are shown. The ’position’-column ranks the results of the author among the number of results of the conference-participants. Joint workshop on natural language processing in biomedicine and its applications Conference on computational natural language learning The results are given in % f-measure.