Introduction

Fundamental notions of combinatorics on words underlie natural language processing. This is not surprising, since combinatorics on words can be seen as the formal study of sets of strings, and sets of strings are fundamental objects in language processing.

Indeed, language processing is obviously a matter of strings. A text or a discourse is a sequence of sentences; a sentence is a sequence of words; a word is a sequence of letters. The most universal levels are those of sentence, word, and letter (or phoneme), but intermediate levels exist, and can be crucial in some languages, between word and letter: a level of morphological elements (e.g. suffixes), and the level of syllables. The discovery of this piling up of levels, and in particular of word level and phoneme level, delighted structuralist linguists in the twentieth century. They termed this inherent, universal feature of human language “double articulation”.

It is a little more intricate to see how sets of strings are involved. There are two main reasons. First, at a point in a linguistic flow of data being processed, you must be able to predict the set of possible continuations after what is already known, or at least to expect any continuation among some set of strings that depends on the language. Second, natural languages are ambiguous, that is a written or spoken portion of text can often be understood or analysed in several ways, and the analyses are handled as a set of strings as long as they cannot be reduced to a single analysis.

Introduction

The chapter presents data structures used to memorize the suffixes of a text and some of their applications. These structures are designed to give a fast access to all factors of the text, and this is the reason why they have a fairly large number of applications in text processing.

Two types of objects are considered in this chapter, digital trees and automata, together with their compact versions. Trees put together common prefixes of the words in the set. Automata gather in addition their common suffixes. The structures are presented in order of decreasing size.

The representation of all the suffixes of a word by an ordinary digital tree called a suffix trie (Section 2.1) has the advantage of being simple but can lead to a memory size that is quadratic in the length of the considered word. The compact tree of suffixes (Section 2.2) is guaranteed to hold in linear memory space.

The minimization (related to automata) of the suffix trie gives the minimal automaton accepting the suffixes and is described in Section 2.4. Compaction and minimization yield the compact suffix automaton of Section 2.5.

Most algorithms that build the structures presented in this chapter work in time O(n × log Card A), for a text of length n, assuming that there is an ordering on the alphabet A. Their execution time is thus linear when the alphabet is finite and fixed. Locating a word of length m in the text then takes O(m × log Card A) time.

Introduction

This chapter is an introductory chapter to the book. It gives general notions, notation, and technical background. It covers, in a tutorial style, the main notions in use in algorithms on words. In this sense, it is a comprehensive exposition of basic elements concerning algorithms on words, automata and transducers, and probability on words.

The general goal of “stringology” we pursue here is to manipulate strings of symbols, to compare them, to count them, to check some properties, and perform simple transformations in an effective and efficient way.

A typical illustrative example of our approach is the action of circular permutations on words, because several of the aspects we mentioned above are present in this example. First, the operation of circular shift is a transduction which can be realized by a transducer. We include in this chapter a section (Section 1.5) on transducers. Transducers will be used in Chapter 3. The orbits of the transformation induced by the circular permutation are the so-called conjugacy classes. Conjugacy classes are a basic notion in combinatorics on words. The minimal element in a conjugacy class is a good representative of a class. It can be computed by an efficient algorithm (actually in linear time). This is one of the algorithms which appear in Section 1.2. Algorithms for conjugacy are again considered in Chapter 2. These words give rise to Lyndon words which have remarkable combinatorial properties already emphasized in Lothaire (1997). We describe in Section 1.2.5 the Lyndon factorization algorithm.

A series of important applications of combinatorics on words has emerged with the development of computerized text and string processing, especially in biology and in linguistics. The aim of this volume is to present, in a unified treatment, some of the major fields of applications. The main topics that are covered in this book are

1. Algorithms for manipulating text, such as string searching, pattern matching, and testing a word for special properties.

2. Efficient data structures for retrieving information on large indexes, including suffix trees and suffix automata.

3. Combinatorial, probabilistic, and statistical properties of patterns in finite words, and more general pattern, under various assumptions on the sources of the text.

4. Inference of regular expressions.

5. Algorithms for repetitions in strings, such as maximal run or tandem repeats.

6. Linguistic text processing, especially analysis of the syntactic and semantic structure of natural language. Applications to language processing with large dictionaries.

7. Enumeration, generation, and sampling of complex combinatorial structures by their encodings in words.

This book is actually the third of a series of books on combinatorics on words. Lothaire's “Combinatorics on Words” appeared in its first printing in 1984 as Volume 17 of the Encyclopedia of Mathematics. It was based on the impulse of M. P. Schützenberger's scientific work. Since then, the theory developed to a large scientific domain. It was reprinted in 1997 in the Cambridge Mathematical Library.

Introduction

Repeated patterns and related phenomena in words are known to play a central role in many facets of computer science, telecommunications, coding, data compression, and molecular biology. One of the most fundamental questions arising in such studies is the frequency of pattern occurrences in another string known as the text. Applications of these results include gene finding in biology, code synchronization, user search in wireless communications, detecting signatures of an attacker in intrusion detection, and discovering repeated strings in the Lempel-Ziv schemes and other data compression algorithms.

In basic pattern matching one finds for a given (or random) pattern w or a set of patterns W and text X how many times W occurs in the text and how long it takes for W to occur in X for the first time. These two problems are not unrelated as we have already seen in Chapter 6. Throughout this chapter we allow patterns to overlap and we count overlapping occurrences separately. For example, w = abab occurs three times in the text = bababababb.

We consider pattern matching problems in a probabilistic framework in which the text is generated by a probabilistic source while the pattern is given. In Chapter 1 various probabilistic sources were discussed. Here we succinctly summarize assumptions adopted in this chapter. In addition, we introduce a new general source known as a dynamical source recently proposed by Vallée. In Chapter 2 algorithmic aspects of pattern matching and various efficient algorithms for finding patterns were discussed.

Introduction

The application of statistical methods to natural language processing has been remarkably successful over the past two decades. The wide availability of text and speech corpora has played a critical role in their success since, as for all learning techniques, these methods rely heavily on data. Many of the components of complex natural language processing systems, for example, text normalizers, morphological or phonological analyzers, part-of-speech taggers, grammars or language models, pronunciation models, context-dependency models, acoustic Hidden-Markov Models (HMMs), are statistical models derived from large data sets using modern learning techniques. These models are often given as weighted automata or weighted finite-state transducers either directly or as a result of the approximation of more complex models.

Weighted automata and transducers are the finite automata and finite-state transducers described in Chapter 1 Section 1.5 with the addition of some weight to each transition. Thus, weighted finite-state transducers are automata in which each transition, in addition to its usual input label, is augmented with an output label from a possibly different alphabet, and carries some weight. The weights may correspond to probabilities or log-likelihoods or they may be some other costs used to rank alternatives. More generally, as we shall see in the next section, they are elements of a semiring set. Transducers can be used to define a mapping between two different types of information sources, for example, word and phoneme sequences.

Introduction

This chapter shows some examples of applications of combinatorics on words to number theory with a brief incursion into physics. These examples have a common feature: the notion of morphism of the free monoid. Such morphisms have been widely studied in combinatorics on words; they generate infinite words which can be considered as highly ordered, and which occur in an ubiquitous way in mathematics, theoretical computer science, and theoretical physics.

The first part of this chapter is devoted to the notion of automatic sequences and uniform morphisms, in connection with the transcendence of formal power series with coefficients in a finite field. Namely it is possible to characterize algebraicity of these series in a simple way: a formal power series is algebraic if and only if the sequence of its coefficients is automatic, that is, if it is the image by a letter-to-letter map of a fixed point of a uniform morphism. This criterion is known as Christol's theorem. A central tool in the study of automatic sequences is the notion of kernel of an infinite word (sequence) over a finite alphabet: this is the set of subsequences obtained by certain decimations. A rephrasing of Christol's theorem is that transcendence of a formal power series over a finite field is equivalent to infiniteness of the kernel of the sequence of its coefficients: this will be illustrated in this chapter.

Introduction

This chapter illustrates the use of words to derive enumeration results and algorithms for sampling and coding.

Given a family C of combinatorial structures, endowed with a size such that the subset Cn of objects of size n is finite, we consider three problems:

1. (i) Counting: determine for all n ≥ 0, the cardinal Card (Cn) of the set Cn of objects with size n.

2. (ii) Sampling: design an algorithm RandC that, for any n, produces a random object uniformly chosen in Cn: in other terms, the algorithm must satisfy P(RandC(n) = O) = 1/Card (Cn) for any object O ∊ Cn.

3. (iii) Optimal coding: construct a function φ that maps injectively objects of C on words of {0, 1}* in such a way that an object O of size n is coded by a word φ(O) of length roughly bounded above by log2 Card (Cn).

These three problems have in common an enumerative flavour, in the sense that they are immediately solved if a list of all objects of size n is available. However, since in general there is an exponential number of objects of size n in the families in which we are interested, this solution is in no way satisfying. For a wide class of so-called decomposable combinatorial structures, including nonambiguous algebraic languages, algorithms with polynomial complexity can be derived from the rather systematic recursive method. Our aim is to explore classes of structures for which an even tighter link exists between counting, sampling, and coding.

Introduction

This chapter introduces various mathematical models and combinatorial algorithms that are used to infer network expressions which appear repeated in a word or are common to a set of words, where by network expression is meant a regular expression without Kleene closure on the alphabet of the input word(s). A network expression on such an alphabet is therefore any expression built up of concatenation and union operators. For example, the expression A(C + G)T concatenates A with the union (C + G) and with T. Inferring network expressions means discovering such expressions which are initially unknown. The only input is the word(s) where the repeated (or common) expressions will be sought. This is in contrast with another problem, we shall not be concerned with, which searches for a known expression in a word(s) both of which are in this case part of the input. The inference of network expressions has many applications, notably in molecular biology, system security, text mining, etc. Because of the richness of the mathematical and algorithmic problems posed by molecular biology, we concentrate on applications in this area. The network expressions considered may therefore contain spacers where by spacer is meant any number of don't care symbols (a don't care is a symbol that matches anything). Constrained spacers are consecutive don't care symbols whose number ranges over a fixed interval of values. Network expressions with don't care symbols but no spacers are called “simple” while network expressions with spacers are called “flexible” if the spacers are unconstrained, and “structured” otherwise.

Introduction

Repetitions (periodicities) in words are important objects that play a fundamental role in combinatorial properties of words and their applications to string processing, such as compression or biological sequence analysis. Using properties of repetitions allows one to speed up pattern matching algorithms.

The problem of efficiently identifying repetitions in a given word is one of the classical pattern matching problems. Recently, searching for repetitions in strings received a new motivation, due to the biosequence analysis. In DNA sequences, successively repeated fragments often bear important biological information and their presence is characteristic for many genomic structures (such as telomer regions). From a practical view-point, satellites and alu-repeats are involved in chromosome analysis and genotyping, and thus are of major interest to genomic researchers. Thus, different biological studies based on the analysis of tandem repeats have been done, and even databases of tandem repeats in certain species have been compiled.

In this chapter, we present a general efficient approach to computing different periodic structures in words. It is based on two main algorithmic techniques – a special factorization of the word and so-called longest extension functions – described in Section 8.3. Different applications of this method are described in Sections 8.4, 8.5, 8.6, 8.7, and 8.8. These sections are preceded by Section 8.2 devoted to combinatorial enumerative properties of repetitions. Bounding the maximal number of repetitions is necessary for proving complexity bounds of corresponding search algorithms.

Introduction

Statistical and probabilistic properties of words in sequences have been of considerable interest in many fields, such as coding theory and reliability theory, and most recently in the analysis of biological sequences. The latter will serve as the key example in this chapter. We only consider finite words.

Two main aspects of word occurrences in biological sequences are: where do they occur and how many times do they occurfi An important problem, for instance, was to determine the statistical significance of a word frequency in a DNA sequence. The naive idea is the following: a word may be significantly rare in a DNA sequence because it disrupts replication or gene expression, (perhaps a negative selection factor), whereas a significantly frequent word may have a fundamental activity with regard to genome stability. Well-known examples of words with exceptional frequencies in DNA sequences are certain biological palindromes corresponding to restriction sites avoided, for instance in E. coli, and the Cross-over Hotspot Instigator sites in several bacteria. Identifying over- and underrepresented words in a particular genome is a very common task in genome analysis.

Statistical methods of studying the distribution of the word locations along a sequence and word frequencies have also been an active field of research; the goal of this chapter is to provide an overview of the state of this research.

Probably the most important data type after vectors and free text is that of symbol strings of varying lengths. This type of data is commonplace in bioinformatics applications, where it can be used to represent proteins as sequences of amino acids, genomic DNA as sequences of nucleotides, promoters and other structures. Partly for this reason a great deal of research has been devoted to it in the last few years. Many other application domains consider data in the form of sequences so that many of the techniques have a history of development within computer science, as for example in stringology, the study of string algorithms.

Kernels have been developed to compute the inner product between images of strings in high-dimensional feature spaces using dynamic programming techniques. Although sequences can be regarded as a special case of a more general class of structures for which kernels have been designed, we will discuss them separately for most of the chapter in order to emphasise their importance in applications and to aid understanding of the computational methods. In the last part of the chapter, we will show how these concepts and techniques can be extended to cover more general data structures, including trees, arrays, graphs and so on.

Certain kernels for strings based on probabilistic modelling of the data-generating source will not be discussed here, since Chapter 12 is entirely devoted to these kinds of methods. There is, however, some overlap between the structure kernels presented here and those arising from probabilistic modelling covered in Chapter 12.