This document is available from http://ilk.kub.nl/~ilk/papers/ilk9901.ps.gz. All rights reserved Induction of Linguistic Knowledge, Tilburg University. Contents 1 License terms 1 2 Installation 3 3 Changes 4 4 Learning algorithms 6

We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger. Based on such a corpus, the tagger-generator automatically builds a tagger which is able to tag new text the same way, diminishing development time for the construction of a tagger considerably. Memory-based tagging shares this advantage with other statistical or machine learning approaches. Additional advantages specific to a memory-based approach include (i) the relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities, (iv) flexible integration of information in case representations, (v) its non-parametric nature, (vi) reasonably good results on unknown words without morphological analysis, and (vii) fast learning and tagging. In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, ad with attractive space and time complexity properties when using IGTree, a tree-based formalism for indexing and searching huge case bases. The use of IGTree has as additional advantage that optimal context size for disambiguation is dynamically computed.

We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging, prepositional-phrase attachment, and base noun phrase chunking. In a first series of experiments we combine memory-based learning with training set editing techniques, in which instances are edited based on their typicality and class prediction strength. Results show that editing exceptional instances (with low typicality or low class prediction strength) tends to harm generalization accuracy. In a second series of experiments we compare memory-based learning and decision-tree learning methods on the same selection of tasks, and find that decision-tree learning often performs worse than memory-based learning. Moreover, the decrease in performance can be linked to the degree of abstraction from exceptions (i.e., pruning or eagerness). We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.

We describe the IGTree learning algorithm, which compresses an instance base into a tree structure. The concept of information gain is used as a heuristic function for performing this compression. IGTree produces trees that, compared to other lazy learning approaches, reduce storage requirements and the time required to compute classifications. Furthermore, we obtained similar or better generalization accuracy with IGTree when trained on two complex linguistic tasks, viz. letter--phoneme transliteration and part-of-speech-tagging, when compared to alternative lazy learning and decision tree approaches (viz., IB1, information-gain-weighted IB1, and C4.5). A third experiment, with the task of word hyphenation, demonstrates that when the mutual differences in information gain of features is too small, IGTree as well as information-gain-weighted IB1 perform worse than IB1. These results indicate that IGTree is a useful algorithm for problems characterized by the availability of a large num...

We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as nemory-based modules. The experiments reported in this paper show competitive results, the Fa= for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% fox' subject detection and 79.0% for object detection.

We present a general architecture for efficient and deterministic morphological analysis based on memory-based learning, and apply it to morphological analysis of Dutch. The system makes direct mappings from letters in context to rich categories that encode morphological boundaries, syntactic class labels, and spelling changes. Both precision and recall of labeled morphemes are over 84% on held-out dictionary test words and estimated to be over 93% in free text.

This pal)or int;roduces a new type of grammar learning algorit;hm, insl)ircd l)y sl,ring edii, dis- tan(;c (Wagner an(t Fis(:hcr, 1974). The algorithm takes a (:oft)us of fiat senl,en(:cs as intml, and rcLurns a corpus of labelled, 1)ra(:keted senl, en(:es. Th( lnel,hod works on pairs of Lured sellt,ellCeS l,ha[, have oBe o1: illore words in (:ommon. When t, wo sentences are (tivi(led int,o t)arLs i;haL m'e Lhc same in 1)ol, h s(mLen(:es and t)arLs that m:e (litlrenL, this interreal,ion is used to find ])m'Ls l, haL are hd;cr(:hmgeablc. These t)arLs m'e tak(m as possible (:onsLii, uenLs same type. Afi,er this aligmnent learning step, the sele(:tion learning s(,c 1) s(l(z(:l,s i,he mosL at)le (:onsl;ihmnl;s fi'om all possible (:onsLiLuent,s. This method was used 1,o booLsLra t) stru(:hrc on the A.TIS (:oftres (Mm'(:us et, al., 1993) and on the OVI'S 1 corpus (Bornmina eL al., 1997). While Lhc results are en(:om'aging (we o})l, aincd up t,o 89.25 % non-crossing l)ra(:ket,s 1)rc(:ision), this paper will 1)oini; ouL some of the shorl,COlnings of our apl)rom:h and will suggest 1)ossible sohd,ions.

The availability of on-line corpora is rapidly changing the field of natural language processing (NLP) from one dominated by theoretical models of often very specific linguistic phenomena to one guided by computational models that simultaneously account for a wide variety of phenomena that occur in real-world text. Thus far, among the best-performing and most robust systems for reading and summarizing large amounts of real-world text are knowledge-based natural language systems. These systems rely heavily on domain-specific, handcrafted knowledge to handle the myriad syntactic, semantic, and pragmatic ambiguities that pervade virtually all aspects of sentence analysis. Not surprisingly, however, generating this knowledge for new domain...

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning algorithms are applied to three variations of the task of morphological analysis. Results show (i) that the generalisation performance of the algorithms is good, and (ii) that the lazy learning algorithm ib1-ig performs best on all three tasks. We conclude that lazy learning of morphological analysis as a classification task is indeed a viable approach; moreover, it has the strong advantages of avoiding the knowledge-acquisition bottleneck, being fast and deterministic in learning and pr...

This paper analyses the relation between the use of similarity in Memory-Based Learning and the notion of backed-off smoothing in statistical language modeling. We show that the two approaches are closely related, and we argue that feature weighting methods in the Memory-Based paradigm can offer the advantage of automatically specifying a suitable domain-specific hierarchy between most specific and most general conditioning information without the need for a large number of parameters. We report two applications of this approach: PP-attachment and POS-tagging. Our method achieves state-of-the-art performance in both domains, and allows the easy integration of diverse information sources, such as rich lexical representations.