Abstract
Annotated data have recently become more important, and thus more abundant, in computational linguistics . They are used as training material for machine learning systems for a wide variety of applications from Parsing to Machine Translation (Quirk et al., 2005). Dependency representation is preferred for many languages because linguistic and semantic information is easier to retrieve from the more direct dependency representation. Dependencies are relations that are defined on words or smaller units where the sentences are divided into its elements called heads and their arguments, e.g. verbs and objects. Dependency parsing aims to predict these dependency relations between lexical units to retrieve information, mostly in the form of semantic interpretation or syntactic structure. Parsing is usually considered as the first step of Natural Language Processing (NLP). To train statistical parsers, a sample of data annotated with necessary information is required. There are different views on how informative or functional representation of natural language sentences should be. There are different constraints on the design process such as: 1) how intuitive (natural) it is, 2) how easy to extract information from it is, and 3) how appropriately and unambiguously it represents the phenomena that occur in natural languages. In this article, a review of statistical dependency parsing for different languages will be made and current challenges of designing dependency treebanks and dependency parsing will be discussed.
TopDependency Grammar
The concept of dependency grammar is usually attributed to Tesnière (1959) and Hays (1964). The dependency theory has since developed, especially with the works of Gross (1964), Gaiffman (1965), Robinson (1970), Mel’čuk (1988), Starosta (1988), Hudson (1984, 1990), Sgall et al. (1986), Barbero et al. (1998), Duchier (2001), Menzel and Schröder (1998), Kruijff (2001).
Dependencies are defined as links between lexical entities (words or morphemes) that connect heads and their dependants. Dependencies may have labels, such as subject, object, and determiner or they can be unlabelled. A dependency tree is often defined as a directed, acyclic graph of links that are defined between words in a sentence. Dependencies are usually represented as trees where the root of the tree is a distinct node. Sometimes dependency links cross. Dependency graphs of this type are non-projective. Projectivity means that in surface structure a head and its dependants can only be separated by other dependants of the same head (and dependants of these dependants). Non-projective dependency trees cannot be translated to phrase structure trees unless treated specially. We can see in Table 1 that the notion of non-projectivity is very common across languages although distribution of it is usually rare in any given language. The fact that it is rare does not make it less important because it is this kind of phenomena that makes natural languages more interesting and that makes all the difference in the generative capacity of a grammar that is suggested to explain natural languages.
Key Terms in this Chapter
Statistical Parser: A group of parsing methods within NLP. The methods have in common that they associate grammar rules with a probability.
Machine Translation (MT): The act of translating something by means of a machine, especially a computer.
Morpheme: The smallest unit of meaning. A word may consist of one morpheme (need), two morphemes (need/less, need/ing) or more (un/happi/ness).
Corpus (corpora plural): A collection of written or spoken material in machine-readable form.
Rule-Based Parser: A parser that uses hand written (designed) rules as opposed to rules that are derived from the data.
Phrase Structure Tree: A structural representation of a sentence in the form of an inverted tree, with each node of the tree labelled according to the phrasal constituent it represents.
Treebank: A text-corpus in which each sentence is annotated with syntactic structure. Syntactic structure is commonly represented as a tree structure. Treebanks can be used in corpus linguistics for studying syntactic phenomena or in computational linguistics for training or testing parsers.