A Flexible POS Tagger Using an Automatically Acquired Language Model

A Flexible POS Tagger Using an Automatically Acquired Language Model* Llufs Mhrquez LSI- UPC c/Jordi Girona 1-3 08034 Barcelona. Catalonia lluism©isi, upc. es Llu/s Padr6 LSI- UPC c/Jordi Girona 1-3 08034 Barcelona. Catalonia padro@isi, upc. es Abstract We present an algorithm that automati- cally learns context constraints using sta- tistical decision trees. We then use the ac- quired constraints in a flexible POS tag- ger. The tagger is able to use informa- tion of any degree: n-grams, automati- cally learned context constraints, linguis- tically motivated manually written con- straints, etc. The sources and kinds of con- straints are unrestricted, and the language model can be easily extended, improving the results. The tagger has been tested and evaluated on the WSJ corpus. 1 Introduction In NLP, it is necessary to model the language in a representation suitable for the task to be performed. The language models more commonly used are based on two main approaches: first, the linguistic ap- proach, in which the model is written by a linguist, generally in the form of rules or constraints (Vouti- lainen and Jgrvinen, 1995). Second, the automatic approach, in which the model is automatically ob- tained from corpora (either raw or annotated) 1 , and consists of n-grams (Garside et al., 1987; Cutting et ah, 1992), rules (Hindle, 1989) or neural nets (Schmid, 1994). In the automatic approach we can distinguish two main trends: The low-level data trend collects statistics from the training corpora in the form of n-grams, probabilities, weights, etc. The high level data trend acquires more sophisticated in- formation, such as context rules, constraints, or de- cision trees (Daelemans et al., 1996; M/~rquez and Rodriguez, 1995; Samuelsson et al., 1996). The ac- quisition methods range from supervised-inductive- learning-from-example algorithms (Quinlan, 1986; *This research has been partially funded by the Span- ish Research Department (CICYT) and inscribed as TIC96-1243-C03-02 I When the model is obtained from annotated corpora we talk about supervised learning, when it is obtained from raw corpora training is considered unsupervised. Aha et al., 1991) to genetic algorithm strategies (Losee, 1994), through the transformation-based error-driven algorithm used in (Brill, 1995), Still another possibility are the hybrid models, which try to join the advantages of both approaches (Vouti- lainen and Padr6, 1997). We present in this paper a hybrid approach that puts together both trends in automatic approach and the linguistic approach. We describe a POS tag- ger based on the work described in (Padr6, 1996), that is able to use bi/trigram information, auto- matically learned context constraints and linguisti- cally motivated manually written constraints. The sources and kinds of constraints are unrestricted, and the language model can be easily extended. The structure of the tagger is presented in figure 1. Language Model . I~:.i:;:;~: I / le~ed | t wri.e. | ... l i.wco us Figure h Tagger architecture. Corpus We also present a constraint-acquisition algo- rithm that uses statistical decision trees to learn con- text constraints from annotated corpora and we use the acquired constraints to feed …

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