DOCSLIB.ORG

Three Dependency-And-Boundary Models for Grammar Induction

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Three Dependency-And-Boundary Models for Grammar Induction Three Dependency-and-Boundary Models for Grammar Induction Valentin I. Spitkovsky Hiyan Alshawi Stanford University and Google Inc. Google Inc., Mountain View, CA, 94043 [email protected] [email protected] Daniel Jurafsky Stanford University, Stanford, CA, 94305 [email protected] Abstract DT NN VBZ IN DT NN [The check] is in [the mail]. We present a new family of models for unsu- | {z } | {z } pervised parsing, Dependency and Boundary Subject Object models, that use cues at constituent bound- Figure 1: A partial analysis of our running example. aries to inform head-outward dependency tree generation. We build on three intuitions that Consider the example in Figure 1. Because the are explicit in phrase-structure grammars but determiner (DT) appears at the left edge of the sen- only implicit in standard dependency formu- tence, it should be possible to learn that determiners lations: (i) Distributions of words that oc- may generally be present at left edges of phrases. cur at sentence boundaries — such as English This information could then be used to correctly determiners — resemble constituent edges. parse the sentence-internal determiner in the mail. (ii) Punctuation at sentence boundaries fur- ther helps distinguish full sentences from Similarly, the fact that the noun head (NN) of the ob- fragments like headlines and titles, allow- ject the mail appears at the right edge of the sentence ing us to model grammatical differences be- could help identify the noun check as the right edge tween complete and incomplete sentences. of the subject NP. As with jigsaw puzzles, working (iii) Sentence-internal punctuation boundaries inwards from boundaries helps determine sentence- help with longer-distance dependencies, since internal structures of both noun phrases, neither of punctuation correlates with constituent edges. which would be quite so clear if viewed separately. Our models induce state-of-the-art depen- dency grammars for many languages without Furthermore, properties of noun-phrase edges are special knowledge of optimal input sentence partially shared with prepositional- and verb-phrase lengths or biased, manually-tuned initializers. units that contain these nouns. Because typical head- driven grammars model valence separately for each class of head, however, they cannot see that the left 1 Introduction fringe boundary, The check, of the verb-phrase is Natural language is ripe with all manner of bound- shared with its daughter’s, check. Neither of these aries at the surface level that align with hierarchical insights is available to traditional dependency for- syntactic structure. From the significance of func- mulations, which could learn from the boundaries tion words (Berant et al., 2006) and punctuation of this sentence only that determiners might have no marks (Seginer, 2007; Ponvert et al., 2010) as sepa- left- and that nouns might have no right-dependents. rators between constituents in longer sentences — to We propose a family of dependency parsing mod- the importance of isolated words in children’s early els that are capable of inducing longer-range im- vocabulary acquisition (Brent and Siskind, 2001) plications from sentence edges than just fertilities — word boundaries play a crucial role in language of their fringe words. Our ideas conveniently lend learning. We will show that boundary information themselves to implementations that can reuse much can also be useful in dependency grammar induc- of the standard grammar induction machinery, in- tion models, which traditionally focus on head rather cluding efficient dynamic programming routines for than fringe words (Carroll and Charniak, 1992). the relevant expectation-maximization algorithms. 2 The Dependency and Boundary Models mented as both head-outward and head-inward au- tomata. (In fact, arbitrary permutations of siblings Our models follow a standard generative story for to a given side of their parent would not affect the head-outward automata (Alshawi, 1996a), restricted likelihood of the modified tree, with the DMV.) We 1 to the split-head case (see below), over lexical word propose to make fuller use of split-head automata’s classes {cw }: first, a sentence root cr is chosen, with head-outward nature by drawing on information in P probability ATTACH (cr | ⋄; L); ⋄ is a special start partially-generated parses, which contain useful pre- symbol that, by convention (Klein and Manning, dictors that, until now, had not been exploited even 2004; Eisner, 1996), produces exactly one child, to in featurized systems for grammar induction (Cohen its left. Next, the process recurses. Each (head) and Smith, 2009; Berg-Kirkpatrick et al., 2010). word ch generates a left-dependent with probability Some of these predictors, including the identity P 1 − STOP( · | L; · · · ), where dots represent additional — or even number (McClosky, 2008) — of already- parameterization on which it may be conditioned. If generated siblings, can be prohibitively expensive in the child is indeed generated, its identity cd is cho- sentences above a short length k. For example, they P sen with probability ATTACH(cd | ch; · · · ), influenced break certain modularity constraints imposed by the by the identity of the parent ch and possibly other pa- charts used in O(k3)-optimized algorithms (Paskin, rameters (again represented by dots). The child then 2001a; Eisner, 2000). However, in bottom-up pars- generates its own subtree recursively and the whole ing and training from text, everything about the yield process continues, moving away from the head, un- — i.e., the ordered sequence of all already-generated til ch fails to generate a left-dependent. At that point, descendants, on the side of the head that is in the an analogous procedure is repeated to ch’s right, this process of spawning off an additional child — is not P time using stopping factors STOP( · | R; · · · ). All parse only known but also readily accessible. Taking ad- trees derived in this way are guaranteed to be projec- vantage of this availability, we designed three new tive and can be described by split-head grammars. models for dependency grammar induction. Instances of these split-head automata have been heavily used in grammar induction (Paskin, 2001b; 2.1 Dependency and Boundary Model One Klein and Manning, 2004; Headden et al., 2009, DBM-1 conditions all stopping decisions on adja- inter alia), in part because they allow for efficient cency and the identity of the fringe word ce — the implementations (Eisner and Satta, 1999, §8) of currently-farthest descendant (edge) derived by head the inside-outside re-estimation algorithm (Baker, ch in the given head-outward direction (dir ∈ {L, R}): 1979). The basic tenet of split-head grammars is P that every head word generates its left-dependents STOP( · | dir; adj, ce ). independently of its right-dependents. This as- In the adjacent case (adj = T), ch is deciding whether sumption implies, for instance, that words’ left- to have any children on a given side: a first child’s and right-valences — their numbers of children subtree would be right next to the head, so the head to each side — are also independent. But it does and the fringe words coincide (ch = ce). In the non- not imply that descendants that are closer to the adjacent case (adj = F), these will be different words head cannot influence the generation of farther and their classes will, in general, not be the same.2 dependents on the same side. Nevertheless, many Thus, non-adjacent stopping decisions will be made popular grammars for unsupervised parsing behave independently of a head word’s identity. Therefore, as if a word had to generate all of its children all word classes will be equally likely to continue to (to one side) — or at least their count — before grow or not, for a specific proposed fringe boundary. allowing any of these children themselves to recurse. For example, production of The check is involves For example, Klein and Manning’s (2004) depen- two non-adjacent stopping decisions on the left: one dency model with valence (DMV) could be imple- by the noun check and one by the verb is, both of which stop after generating a first child. In DBM-1, 1Unrestricted head-outward automata are strictly more pow- erful (e.g., they recognize the language anbn in finite state) than 2Fringe words differ also from other standard dependency the split-head variants, which process one side before the other. features (Eisner, 1996, §2.3): parse siblings and adjacent words. quantity Revenue: $3.57 billion, the time 1:11am, DT NN VBZ IN DT NN ♦ and the like — tend to be much shorter than com- The check is in the mail . plete sentences. The new root-attachment factors 0 z }| { could further track that incomplete sentences gener- P P P = (1 − STOP(⋄ | L; T)) × ATTACH (VBZ | ⋄; L) ally lack verbs, in contrast to other short sentences, × (1 − PSTOP( · | L; T, VBZ)) × PATTACH (NN | VBZ; L) × (1 − PSTOP( · | R; T, VBZ)) × PATTACH (IN | VBZ; R) e.g., Excerpts follow:, Are you kidding?, Yes, he × PSTOP( · | L; F, DT) // VBZ × PSTOP( · | R; F, NN) // VBZ did., It’s huge., Indeed it is., I said, ‘NOW?’, “Ab- 2 2 × (1 − PSTOP( · | L; T, NN)) × PATTACH (DT | NN; L) solutely,” he said., I am waiting., Mrs. Yeargin de- × (1 − PSTOP( · | R; T, IN)) × PATTACH (NN | IN; R) 2 2 clined., McGraw-Hill was outraged., “It happens.”, × PSTOP( · | R; T, NN) × PSTOP( · | L; F, DT) // NN I’m OK, Jack., Who cares?, Never mind. and so on. × PSTOP( · | L; T, IN) × PSTOP( · | R; F, NN) // IN P2 P2 × STOP( · | L; T, DT) × STOP( · | R; T, DT) All other attachment probabilities PATTACH(cd | ch; dir) × PSTOP(⋄ | L; F) × PSTOP(⋄ | R; T) . | {z } | {z } remain unchanged, as in DBM-1. In practice, comp 1 1 can indicate presence of sentence-final punctuation. Figure 2: Our running example — a simple sentence and its unlabeled dependency parse structure’s probability, as 2.3 Dependency and Boundary Model Three factored by DBM-1; highlighted comments specify heads DBM-3 adds further conditioning on punctuation associated to non-adjacent stopping probability factors.
Load more

Recommended publications
  • Chapter 14: Dependency Parsing
    Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2021. All rights reserved. Draft of September 21, 2021. CHAPTER 14 Dependency Parsing The focus of the two previous chapters has been on context-free grammars and constituent-based representations. Here we present another important family of dependency grammars grammar formalisms called dependency grammars. In dependency formalisms, phrasal constituents and phrase-structure rules do not play a direct role. Instead, the syntactic structure of a sentence is described solely in terms of directed binary grammatical relations between the words, as in the following dependency parse: root dobj det nmod (14.1) nsubj nmod case I prefer the morning flight through Denver Relations among the words are illustrated above the sentence with directed, labeled typed dependency arcs from heads to dependents. We call this a typed dependency structure because the labels are drawn from a fixed inventory of grammatical relations. A root node explicitly marks the root of the tree, the head of the entire structure. Figure 14.1 shows the same dependency analysis as a tree alongside its corre- sponding phrase-structure analysis of the kind given in Chapter 12. Note the ab- sence of nodes corresponding to phrasal constituents or lexical categories in the dependency parse; the internal structure of the dependency parse consists solely of directed relations between lexical items in the sentence. These head-dependent re- lationships directly encode important information that is often buried in the more complex phrase-structure parses. For example, the arguments to the verb prefer are directly linked to it in the dependency structure, while their connection to the main verb is more distant in the phrase-structure tree.
    [Show full text]
  • Dependency Grammars and Parsers
    Dependency Grammars and Parsers Deep Processing for NLP Ling571 January 30, 2017 Roadmap Dependency Grammars Definition Motivation: Limitations of Context-Free Grammars Dependency Parsing By conversion to CFG By Graph-based models By transition-based parsing Dependency Grammar CFGs: Phrase-structure grammars Focus on modeling constituent structure Dependency grammars: Syntactic structure described in terms of Words Syntactic/Semantic relations between words Dependency Parse A dependency parse is a tree, where Nodes correspond to words in utterance Edges between nodes represent dependency relations Relations may be labeled (or not) Dependency Relations Speech and Language Processing - 1/29/17 Jurafsky and Martin 5 Dependency Parse Example They hid the letter on the shelf Why Dependency Grammar? More natural representation for many tasks Clear encapsulation of predicate-argument structure Phrase structure may obscure, e.g. wh-movement Good match for question-answering, relation extraction Who did what to whom Build on parallelism of relations between question/relation specifications and answer sentences Why Dependency Grammar? Easier handling of flexible or free word order How does CFG handle variations in word order? Adds extra phrases structure rules for alternatives Minor issue in English, explosive in other langs What about dependency grammar? No difference: link represents relation Abstracts away from surface word order Why Dependency Grammar? Natural efficiencies: CFG: Must derive full trees of many
    [Show full text]
  • Dependency Grammar and the Parsing of Chinese Sentences*
    Dependency Grammar and the Parsing of Chinese Sentences* LAI Bong Yeung Tom Department of Chinese, Translation and Linguistics City University of Hong Kong Department of Computer Science and Technology Tsinghua University, Beijing E-mail: [email protected] HUANG Changning Department of Computer Science and Technology Tsinghua University, Beijing Abstract Dependency Grammar has been used by linguists as the basis of the syntactic components of their grammar formalisms. It has also been used in natural langauge parsing. In China, attempts have been made to use this grammar formalism to parse Chinese sentences using corpus-based techniques. This paper reviews the properties of Dependency Grammar as embodied in four axioms for the well-formedness conditions for dependency structures. It is shown that allowing multiple governors as done by some followers of this formalism is unnecessary. The practice of augmenting Dependency Grammar with functional labels is discussed in the light of building functional structures when the sentence is parsed. This will also facilitate semantic interpretion. 1 Introduction Dependency Grammar (DG) is a grammatical theory proposed by the French linguist Tesniere.[1] Its formal properties were then studied by Gaifman [2] and his results were brought to the attention of the linguistic community by Hayes.[3] Robinson [4] considered the possiblity of using this grammar within a transformation-generative framework and formulated four axioms for the well-formedness of dependency structures. Hudson [5] adopted Dependency Grammar as the basis of the syntactic component of his Word Grammar, though he allowed dependency relations outlawed by Robinson's axioms. Dependency Grammar is concerned directly with individual words.
    [Show full text]
  • Dependency Grammar
    Dependency Grammar Introduc)on Christopher Manning Dependency Grammar and Dependency Structure Dependency syntax postulates that syntac)c structure consists of relaons between lexical items, normally binary asymmetric relaons (“arrows”) called dependencies submitted Bills were by on Brownback ports Senator Republican and immigration of Kansas Christopher Manning Dependency Grammar and Dependency Structure Dependency syntax postulates that syntac)c structure consists of relaons between lexical items, normally binary asymmetric relaons (“arrows”) called dependencies submitted nsubjpass auxpass prep The arrows are Bills were by commonly typed prep pobj with the name of on Brownback pobj nn appos grammacal ports Senator Republican relaons (subject, cc conj prep preposi)onal object, and immigration of apposi)on, etc.) pobj Kansas Christopher Manning Dependency Grammar and Dependency Structure Dependency syntax postulates that syntac)c structure consists of relaons between lexical items, normally binary asymmetric relaons (“arrows”) called dependencies submitted The arrow connects a nsubjpass auxpass prep head (governor, Bills were by superior, regent) with a prep pobj dependent (modifier, on inferior, subordinate) Brownback pobj nn appos ports Senator Republican Usually, dependencies cc conj prep form a tree (connected, and immigration of acyclic, single-head) pobj Kansas Christopher Manning Dependency Grammar and Dependency Structure ROOT Discussion of the outstanding issues was completed . • Some people draw the arrows one way; some the other way! • Tesnière had them point from head to dependent… • Usually add a fake ROOT so every word is a dependent of precisely 1 other node Christopher Manning Dependency Grammar/Parsing History • The idea of dependency structure goes back a long way • To Pāṇini’s grammar (c.
    [Show full text]
  • Dependency Grammar Induction Via Bitext Projection Constraints
    Dependency Grammar Induction via Bitext Projection Constraints Kuzman Ganchev and Jennifer Gillenwater and Ben Taskar Department of Computer and Information Science University of Pennsylvania, Philadelphia PA, USA {kuzman,jengi,taskar}@seas.upenn.edu Abstract ing recent interest in transferring linguistic re- sources from one language to another via parallel Broad-coverage annotated treebanks nec- text. For example, several early works (Yarowsky essary to train parsers do not exist for and Ngai, 2001; Yarowsky et al., 2001; Merlo many resource-poor languages. The wide et al., 2002) demonstrate transfer of shallow pro- availability of parallel text and accurate cessing tools such as part-of-speech taggers and parsers in English has opened up the pos- noun-phrase chunkers by using word-level align- sibility of grammar induction through par- ment models (Brown et al., 1994; Och and Ney, tial transfer across bitext. We consider 2000). generative and discriminative models for Alshawi et al. (2000) and Hwa et al. (2005) dependency grammar induction that use explore transfer of deeper syntactic structure: word-level alignments and a source lan- dependency grammars. Dependency and con- guage parser (English) to constrain the stituency grammar formalisms have long coex- space of possible target trees. Unlike isted and competed in linguistics, especially be- previous approaches, our framework does yond English (Mel’cuk,ˇ 1988). Recently, depen- not require full projected parses, allowing dency parsing has gained popularity as a simpler, partial, approximate transfer through lin- computationally more efficient alternative to con- ear expectation constraints on the space stituency parsing and has spurred several super- of distributions over trees.
    [Show full text]
  • Towards an Implelnentable Dependency Grammar
    Towards an implementable dependency grammar Timo JKrvinen and Pasi Tapanainen Research Unit for Multilingual Language Technology P.O. Box 4, FIN-00014 University of Helsinki, Finland Abstract such as a context-free phrase structure gram- mar. This leads us to a situation where the Syntactic models should be descriptively ade- parsing problem is extremely hard in the gen- I quate and parsable. A syntactic description is eral, theoretical case, but fortunately parsable autonomous in the sense that it has certain ex- in practise. Our result shows that, while in gen- plicit formal properties. Such a description re- eral we have an NP-hard parsing problem, there i lates to the semantic interpretation of the sen- is a specific solution for the given grammar that tences, and to the surface text. As the formal- can be run quickly. Currently, the speed of the ism is implemented in a broad-coverage syntac- parsing system I is several hundred words per tic parser, we concentrate on issues that must I second. be resolved by any practical system that uses In short, the grammar should be empirically such models. The correspondence between the motivated. We have all the reason to believe structure and linear order is discussed. ! that if a linguistic analysis rests on a solid de- scriptive basis, analysis tools based on the the- 1 Introduction ory would be more useful for practical purposes. i The aim of this paper is to define a dependency We are studying the possibilities of using com- grammar framework which is both linguistically putational implementation as a developing and motivated and computationally parsable.
    [Show full text]
  • Dependency Parsing
    Dependency Parsing Sandra Kubler¨ LSA Summer Institute 2017 Dependency Parsing 1(16) Dependency Syntax ◮ The basic idea: ◮ Syntactic structure consists of lexical items, linked by binary asymmetric relations called dependencies. ◮ In the words of Lucien Tesni`ere: ◮ La phrase est un ensemble organis´e dont les ´el´ements constituants sont les mots. [1.2] Tout mot qui fait partie d’une phrase cesse par lui-mˆeme d’ˆetre isol´ecomme dans le dictionnaire. Entre lui et ses voisins, l’esprit aper¸coit des connexions, dont l’ensemble forme la charpente de la phrase. [1.3] Les connexions structurales ´etablissent entre les mots des rapports de d´ependance. Chaque connexion unit en principe un terme sup´erieur `aun terme inf´erieur. [2.1] Le terme sup´erieur re¸coit le nom de r´egissant. Le terme inf´erieur re¸coit le nom de subordonn´e. Ainsi dans la phrase Alfred parle [. ], parle est le r´egissant et Alfred le subordonn´e. [2.2] Dependency Parsing 2(16) Dependency Syntax ◮ The basic idea: ◮ Syntactic structure consists of lexical items, linked by binary asymmetric relations called dependencies. ◮ In the words of Lucien Tesni`ere: ◮ The sentence is an organized whole, the constituent elements of which are words. [1.2] Every word that belongs to a sentence ceases by itself to be isolated as in the dictionary. Between the word and its neighbors, the mind perceives connections, the totality of which forms the structure of the sentence. [1.3] The structural connections establish dependency relations between the words. Each connection in principle unites a superior term and an inferior term.
    [Show full text]
  • 1 Dependency Grammar
    Christopher Manning Dependency Grammar and Dependency Structure Dependency Dependency syntax postulates that syntac)c structure consists of Grammar relaons between lexical items, normally binary asymmetric relaons (“arrows”) called dependencies submitted Bills were by Introduc)on on Brownback ports Senator Republican and immigration of Kansas Christopher Manning Christopher Manning Dependency Grammar and Dependency Grammar and Dependency Structure Dependency Structure Dependency syntax postulates that syntac)c structure consists of Dependency syntax postulates that syntac)c structure consists of relaons between lexical items, normally binary asymmetric relaons between lexical items, normally binary asymmetric relaons (“arrows”) called dependencies relaons (“arrows”) called dependencies submitted submitted nsubjpass auxpass prep The arrow connects a nsubjpass auxpass prep head (governor, The arrows are Bills were by Bills were by prep pobj superior, regent) with a prep pobj commonly typed dependent (modifier, on on with the name of Brownback inferior, subordinate) Brownback pobj nn appos pobj nn appos grammacal ports Senator Republican ports Senator Republican relaons (subject, cc conj prep Usually, dependencies cc conj prep preposi)onal object, and immigration of form a tree (connected, and immigration of apposi)on, etc.) pobj acyclic, single-head) pobj Kansas Kansas Christopher Manning Christopher Manning Dependency Grammar and Dependency Structure Dependency Grammar/Parsing History • The idea of dependency structure goes back a long way • To Pāṇini’s grammar (c. 5th century BCE) • Basic approach of 1st millennium Arabic grammarians ROOT Discussion of the outstanding issues was completed . • Cons)tuency is a new-fangled inven)on • 20th century inven)on (R.S. Wells, 1947) • Modern dependency work o^en linked to work of L.
    [Show full text]
  • Dependency Grammars
    Introduction Phrase Structures Dependency Grammars Dependency Grammars Syntactic Theory Winter Semester 2009/2010 Antske Fokkens Department of Computational Linguistics Saarland University 27 October 2009 Antske Fokkens Syntax — Dependency Grammar 1/42 Introduction Phrase Structures Dependency Grammars Outline 1 Introduction 2 Phrase Structures 3 Dependency Grammars Introduction Dependency relations Properties of Dependencies Antske Fokkens Syntax — Dependency Grammar 2/42 Introduction Phrase Structures Dependency Grammars Outline 1 Introduction 2 Phrase Structures 3 Dependency Grammars Introduction Dependency relations Properties of Dependencies Antske Fokkens Syntax — Dependency Grammar 3/42 Introduction Phrase Structures Dependency Grammars Overview of this lecture In the next three lectures, we will discuss Dependency Grammars: Dependencies and Phrase Structures: basic objectives of syntactic analysis properties of phrase structure grammars Basic definitions of Dependencies What are dependencies? Example analyses Differences and Relations between Dependencies and Phrase Structures Syntactic Theory/CL and Dependencies Meaning to Text Theory Prague Dependency Treebank Antske Fokkens Syntax — Dependency Grammar 4/42 Introduction Phrase Structures Dependency Grammars Syntactic Analysis Syntax investigates the structure of expressions Some reasons for performing syntactic analysis: To understand something about how language works To analyze language: how can we relate speech/written text to meaning? Antske Fokkens Syntax — Dependency Grammar
    [Show full text]
  • Enhancing Freeling Rule-Based Dependency Grammars with Subcategorization Frames
    Enhancing FreeLing Rule-Based Dependency Grammars with Subcategorization Frames Marina Lloberes Irene Castellon´ Llu´ıs Padro´ U. de Barcelona U. de Barcelona U. Politecnica` de Catalunya Barcelona, Spain Barcelona, Spain Barcelona, Spain [email protected] [email protected] [email protected] Abstract explicitly relate the improvements in parser per- formance to the addition of subcategorization. Despite the recent advances in parsing, This paper analyses in detail how subcatego- significant efforts are needed to improve rization frames acquired from an annotated cor- the current parsers performance, such as pus and distributed among fine-grained classes in- the enhancement of the argument/adjunct crease accuracy in rule-based dependency gram- recognition. There is evidence that verb mars. subcategorization frames can contribute to The framework used is that of the FreeLing parser accuracy, but a number of issues re- Dependency Grammars (FDGs) for Spanish and main open. The main aim of this paper is Catalan, using enriched lexical-syntactic informa- to show how subcategorization frames ac- tion about the argument structure of the verb. quired from a syntactically annotated cor- FreeLing (Padro´ and Stanilovsky, 2012) is an pus and organized into fine-grained classes open-source library of multilingual Natural Lan- can improve the performance of two rule- guage Processing (NLP) tools that provide linguis- based dependency grammars. tic analysis for written texts. The FDGs are the core of the FreeLing dependency parser, the Txala 1 Introduction Parser (Atserias et al., 2005). Statistical parsers and rule-based parsers have ad- The remainder of this paper is organized as fol- vanced over recent years.
    [Show full text]
  • Introduction to Dependency Grammar Dependency Grammar Overview
    Introduction Dependency Grammar Introduction to Dependency Grammar ◮ Not a coherent grammatical framework: wide range of different kinds of DG ◮ just as there are wide ranges of ”generative syntax” HS Current Approaches to Dependency Parsing ◮ Different core ideas than phrase structure grammar SS 2010 ◮ We will base a lot of our discussion on Mel’ˇcuk (1988) Dependency grammar is important for those interested in CL: ◮ Increasing interest in dependency-based approaches to Thanks to Markus Dickinson, Joakim Nivre and Sandra Kubler.¨ syntactic parsing in recent years (e.g., CoNLL-X shared task, 2006) Introduction to Dependency Grammar 1(29) Introduction to Dependency Grammar 2(29) Introduction Introduction Overview: constituency Overview: dependency Syntactic structure consists of lexical items, linked by binary asymmetric relations called dependencies. (1) Small birds sing loud songs obj What you might be more used to seeing: nmod sbj nmod S Small birds sing loud songs NP VP sing Small birds sing NP sbj obj birds songs loud songs nmod nmod small loud Introduction to Dependency Grammar 3(29) Introduction to Dependency Grammar 4(29) Introduction Introduction Constituency vs. Relations Simple relation example ◮ DG is based on relationships between words, i.e., dependency relations For the sentence John loves Mary, we have the relations: ◮ A B means A governs B or B depends on A ... → ◮ Dependency relations can refer to syntactic properties, ◮ loves subj John semantic properties, or a combination of the two → ◮ loves Mary Some variants of DG separate syntactic and semantic relations obj → → by representing different layers of dependencies Both John and Mary depend on loves, which makes loves the head, ◮ These relations are generally things like subject, or root, of the sentence (i.e., there is no word that governs loves) object/complement, (pre-/post-)adjunct, etc.
    [Show full text]
  • Universal Dependencies
    The <richer> the people, the <bigger> the crates the more <literal> the <better> the less <elaborate> you can be the <better> The <slower> the <better> Linguistic Institute 2017 the <higher> the temperature the <darker> the malt Corpus Linguistics The <older> a database is, the Treebanks & Dependencies <richer> it becomes The <higher> the clay content, the more <sticky> the Nathan Schneider soil The <fresher> the [email protected] soot, the <longer> it needed Amir Zeldes the <higher> the number, the <greater> [email protected] the protection The <older> you are the <greater> the chance The <smaller> the mesh the more <expensive> the net 1 Taking Stock Thus far in this course: • Why Corpora? ‣ Issues in assembling corpora • Markup, tokenization, metadata • Searching, frequency, collocations • Annotation: POS 2 Coming Attractions • Syntactic Treebanks ‣ Annotation: Universal Dependencies • Computational Lexical Semantics ‣ Annotation: Entity Types ‣ WordNet, Frame Semantics, Distributional Vectors 3 POS Tags Aren’t Everything • A POS tag helps narrow down what grammatical constructions a word may participate in. ‣ The main challenge in: “Will Trey Gowdy Benghazi Trump with inquiries?” ‣ But tag doesn’t exactly specify how the token relates to other tokens in the sentence. • We want to be able to search corpora for words in certain syntactic contexts. Helps us answer questions like: ‣ When does adverbial home tend to precede vs. follow the object? (Fillmore 1992, p. 48: take home the leftovers vs. take the leftovers home) ‣ What kinds of nouns prefer to be subjects vs. objects? ‣ Which verbs can take infinitival complements? 4 Ambiguity beyond POS • Lots of constructions = lots of ambiguity! Sometimes humans even notice it: ‣ PP attachment: “Illinois Sends Bill Allowing Gay Marriage to Governor” ‣ Adjective attachment: “Police Shoot Dead Suspect Inside L.A.
    [Show full text]