m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

A central fact about a word is how common it is. The information is particularly valuable for language learners, as it immediately indicates how important it is to learn a word. With the advent of large computerised language corpora, it is for the first time possible to meet the demand. Both Longman Dictionaries and Collins COBUILD decided to present frequency information explicitly in new editions of their learners' dictionaries. The paper describes how this was done at Longman, and the various issues encountered along the way. It also compares the Longman and Collins COBUILD lists.

Natural Language Processing (NLP) is concerned with processing ordinary, unrestricted text. This work takes a new approach to a traditional NLP task, using neural computing methods. A parser which has been successfully implemented is described. It is a hybrid system, in which neural processors operate within a rule based framework. The neural processing components belong to the class of Generalized Single Layer Networks (GSLN). In general, supervised, feed-forward networks need more than one layer to process data. However, in some cases data can be pre-processed with a non-linear transformation, and then presented in a linearly separable form for subsequent processing by a single layer net. Such networks o er advantages of functional transparency and operational speed. For our parser, the initial stage of processing maps linguistic data onto a higher order representation, which can then be analysed by a single layer network. This transformation is supported by information theoretic analysis. Three di erent algorithms for the neural component were investigated. Single layer nets can be trained by nding weight adjustments based on (a) factors proportional to the input, as in the Perceptron, (b) factors proportional to the existing weights, and (c) an error minimization method. In our experiments generalization ability varies little � method (b) is used for a prototype parser. This is available via telnet.

There are two main methodologies for constructing the knowledge base of a natural language analyser: the linguis- tic and the data"driven. Recent state-of- the-art part-of-speech taggers are based on the data"driven approach. Because of the known feasibility of the linguistic rule-based approach at related levels of description, the success of the data" driven approach in part-of-speech analysis may appear surprising. In this paper, a case is made for the syntactic nature of part-of-speech tagging. A new tagger of English that uses only linguistic distributional rules is outlined and empirically evaluated. Tested against a benchmark corpus of 38,000 words of previously unseen text, this syntax-based system reaches an accuracy of above 99%. Compared to the 95-97% accuracy of its best competitors, this result suggests the feasibility of the linguistic approach also in part-of-speech analysis.

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This project describes a method which can automatically infer word classification. Previous systems designed to assign parts-of-speech to words sought the use of training data or were built upon rules devised by experts in linguistics. The report details the use of an unsupervised approach that can reduce significantly the reliance on prior linguistic intuition. The study looks in to how words behave relative to the function words. As these are the most common words, there is a great deal of information that can be attained. It was possible to analyse how the content words from a given body of text were distributed with respect to the function words. This information could be used as a profile, and therefore content words with a similar profile against the function words could be assumed to be of similar word class. Agglomerative hierarchical clustering techniques were applied to partition words into different clusters. Words that were deemed similar were grouped together, and thus, each cluster should contain words that posses the same part-of-speech. This project performed many experiments to investigate how the many factors affected the overall clustering performance, in order to find the optimal parameters. The results report an accuracy of 87% when performed on the LOB corpus. Experiments were also carried out with an alternative Spanish corpus and the clustering accuracy achieved 85%. Semantic clustering was also observed indicating the effectiveness of the described approach for the task of automatically acquiring word classification.

We present a new, INTEX-compatible formalism for the description of distributional constraints, ELAG (Elimination of lexical ambiguity by grammars). The constraints may be checked against text, and the lexical ambiguity of the text may thus be partly resolved. We describe and exemplify the main properties of ELAG with the aid of simple rules, formalizing exploitable constraints. We specify in detail the effect of applying an ELAG rule or grammar to a text. We examine the practical properties of the formalism from the point of view of a rule writer. We describe our evaluation procedure for the lexical disambiguation results.

In this article, we present and evaluate a method for training a statistical partof-speech tagger on data from written language and then adapting it to the requirements of tagging a corpus of transcribed spoken language, in our case spoken Swedish. This is currently a significant problem for many research groups working with spoken language, since the availability of tagged training data from spoken language is still very limited for most languages. The overall accuracy of the tagger developed for spoken Swedish is quite respectable, varying from 95% to 97 % depending on the tagset used. In conclusion, we argue that the method presented here gives good tagging accuracy with relatively little effort.

In this paper we investigate the use of machine learning techniques to classify a wide range of non-sentential utterance types in dialogue, a necessary first step in the interpretation of such fragments. We train different learners on a set of contextual features that can be extracted from PoS information. Our results achieve an 87 % weighted f-score—a 25 % improvement over a simple rule-based algorithm baseline. Keywords Non-sentential utterances, machine learning, corpus analysis 1