Recognizing shallow linguistic patterns , such as basic syntactic relationships between words , is a common task in applied natural language and text processing . The common practice for approaching this task is by tedious manual definition of possible pattern structures , often in the form of regular expressions or finite automata . This paper presents a novel memory-based learning method that recognizes shallow patterns in new text based on a bracketed training corpus . The training data are stored as-is , in efficient suffix-tree data structures . Generalization is performed on-line at recognition time by comparing subsequences of the new text to positive and negative evidence in the corpus . This way , no information in the training is lost , as can happen in other learning systems that construct a single generalized model at the time of training . The paper presents experimental results for recognizing noun phrase , subject-verb and verb-object patterns in English . Since the learning approach enables easy porting to new domains , we plan to apply it to syntactic patterns in other languages and to sub-language patterns for information extraction . Identifying local patterns of syntactic sequences and relationships is a fundamental task in natural language processing ( NLP ) . Such patterns may correspond to syntactic phrases , like noun phrases , or to pairs of words that participate in a syntactic relationship , like the heads of a verb-object relation . Such patterns have been found useful in various application areas , including information extraction , text summarization , and bilingual alignment . Syntactic patterns are useful also for many basic computational linguistic tasks , such as statistical word similarity and various disambiguation problems . One approach for detecting syntactic patterns is to obtain a full parse of a sentence and then extract the required patterns . However , obtaining a complete parse tree for a sentence is difficult in many cases , and may not be necessary at all for identifying most instances of local syntactic patterns . An alternative approach is to avoid the complexity of full parsing and instead to rely only on local information . A variety of methods have been developed within this framework , known as shallow parsing , chunking , local parsing etc . ( e.g. , ( Abney , 1991 ; Greffenstette , 1993 ) ) . These works have shown that it is possible to identify most instances of local syntactic patterns by rules that examine only the pattern itself and its nearby context . Often , the rules are applied to sentences that were tagged by partof-speech ( POS ) and are phrased by some form of regular expressions or finite state automata . Manual writing of local syntactic rules has become a common practice for many applications . However , writing rules is often tedious and time consuming . Furthermore , extending the rules to different languages or sub-language domains can require substantial resources and expertise that are often not available . As in many areas of NLP , a learning approach is appealing . Surprisingly , though , rather little work has been devoted to learning local syntactic patterns , mostly noun phrases ( Ramshaw and Marcus , 1995 ; Vilain and Day , 1996 ) . This paper presents a novel general learning approach for recognizing local sequential patterns , that may be perceived as falling within the memorybased learning paradigm . The method utilizes a part-of-speech tagged training corpus in which all instances of the target pattern are marked ( bracketed ) . The training data are stored as-is in suffix-tree data structures , which enable linear time searching for subsequences in the corpus . The memory-based nature of the presented algorithm stems from its deduction strategy : a new instance of the target pattern is recognized by examining the raw training corpus , searching for positive and negative evidence with respect to the given test sequence . No model is created for the training corpus , and the raw examples are not converted to any other representation . Consider the following example 1 . Suppose we 1We use here the POS tags : DT ----determiner , ADJ = adjective , hDV = adverb , C0NJ = conjunction , VB = verb , PP = preposition , NN = singular noun , and NNP ----plural noun . want to decide whether the candidate sequence DT ADJ ADJ NN NNP is a noun phrase ( NP ) by comparing it to the training corpus . A good match would be if the entire sequence appears as-is several times in the corpus . However , due to data sparseness , an exact match can not always be expected . A somewhat weaker match may be obtained if we consider sub-parts of the candidate sequence ( called tiles ) . For example , suppose the corpus contains noun phrase instances with the following structures : ( i ) DT ADJ ADJ NN NN ( 2 ) DT ADJ NN NNP The first structure provides positive evidence that the sequence " DT ADJ ADJ NN " is a possible NP prefix while the second structure provides evidence for " ADJ NN NNP " being an NP suffix . Together , these two training instances provide positive evidence that covers the entire candidate . Considering evidence for sub-parts of the pattern enables us to generalize over the exact structures that are present in the corpus . Similarly , we also consider the negative evidence for such sub-parts by noting where they occur in the corpus without being a corresponding part of a target instance . The proposed method , as described in detail in the next section , formalizes this type of reasoning . It searches specialized data structures for both positive and negative evidence for sub-parts of the candidate structure , and considers additional factors such as context and evidence overlap . Section 3 presents experimental results for three target syntactic patterns in English , and Section 4 describes related work . We have presented a novel general schema and a particular instantiation of it for learning sequential patterns . Applying the method to three syntactic patterns in English yielded positive results , suggesting its applicability for recognizing local linguistic patterns . In future work we plan to investigate a datadriven approach for optimal selection and weighting of statistical features of candidate scores , as well as to apply the method to syntactic patterns of Hebrew and to domain-specific patterns for information extraction .

Challenge: Existing approaches to linguistic pattern recognition have issues which emerge from difficulties in parse tree acquisition or tedious and time consuming manual rule writing.
Approach: They propose a memory-based learning method that recognizes patterns by matching new texts against a bracketed training corpus stored in an efficient suffix-tree data structure.
Outcome: The proposed model achieves positive results in recognizing the English noun phase, subject-verb and verb-object patterns.