Tagging and parsing with cascaded Markov models : automation of corpus annotation

This thesis presents new techniques for parsing natural language. They are based on Markov Models, which are commonly used in part-of-speech tagging for sequential processing on the world level. We show that Markov Models can be successfully applied to other levels of syntactic processing. first two classification task are handled: the assignment of grammatical functions and the labeling of non-terminal nodes. Then, Markov Models are used to recognize hierarchical syntactic structures. Each layer of a structure is represented by a separate Markov Model. The output of a lower layer is passed as input to a higher layer, hence the name: Cascaded Markov Models. Instead of simple symbols, the states emit partial context-free structures. The new techniques are applied to corpus annotation and partial parsing and are evaluated using corpora of different languages and domains.Ausgehend von Markov-Modellen, die für das Part-of-Speech-Tagging eingesetzt werden, stellt diese Arbeit Verfahren vor, die Markov-Modelle auch auf weiteren Ebenen der syntaktischen Verarbeitung erfolgreich nutzen. Dies betrifft zum einen Klassifikationen wie die Zuweisung grammatischer Funktionen und die Bestimmung von Kategorien nichtterminaler Knoten, zum anderen die Zuweisung hierarchischer, syntaktischer Strukturen durch Markov-Modelle. Letzteres geschieht durch die Repräsentation jeder Ebene einer syntaktischen Struktur durch ein eigenes Markov-Modell, was den Namen des Verfahrens prägt: Kaskadierte Markov-Modelle. Deren Zustände geben anstelle atomarer Symbole partielle kontextfreie Strukturen aus. Diese Verfahren kommen in der Korpusannotation und dem partiellen Parsing zum Einsatz und werden anhand mehrerer Korpora evaluiert

Complexity of Lexical Descriptions and its Relevance to Partial Parsing

In this dissertation, we have proposed novel methods for robust parsing that integrate the flexibility of linguistically motivated lexical descriptions with the robustness of statistical techniques. Our thesis is that the computation of linguistic structure can be localized if lexical items are associated with rich descriptions (supertags) that impose complex constraints in a local context. However, increasing the complexity of descriptions makes the number of different descriptions for each lexical item much larger and hence increases the local ambiguity for a parser. This local ambiguity can be resolved by using supertag co-occurrence statistics collected from parsed corpora. We have explored these ideas in the context of Lexicalized Tree-Adjoining Grammar (LTAG) framework wherein supertag disambiguation provides a representation that is an almost parse. We have used the disambiguated supertag sequence in conjunction with a lightweight dependency analyzer to compute noun groups, verb groups, dependency linkages and even partial parses. We have shown that a trigram-based supertagger achieves an accuracy of 92.1‰ on Wall Street Journal (WSJ) texts. Furthermore, we have shown that the lightweight dependency analysis on the output of the supertagger identifies 83‰ of the dependency links accurately. We have exploited the representation of supertags with Explanation-Based Learning to improve parsing effciency. In this approach, parsing in limited domains can be modeled as a Finite-State Transduction. We have implemented such a system for the ATIS domain which improves parsing eciency by a factor of 15. We have used the supertagger in a variety of applications to provide lexical descriptions at an appropriate granularity. In an information retrieval application, we show that the supertag based system performs at higher levels of precision compared to a system based on part-of-speech tags. In an information extraction task, supertags are used in specifying extraction patterns. For language modeling applications, we view supertags as syntactically motivated class labels in a class-based language model. The distinction between recursive and non-recursive supertags is exploited in a sentence simplification application

Tagging and parsing with cascaded Markov models : automation of corpus annotation

This thesis presents new techniques for parsing natural language. They are based on Markov Models, which are commonly used in part-of-speech tagging for sequential processing on the world level. We show that Markov Models can be successfully applied to other levels of syntactic processing. first two classification task are handled: the assignment of grammatical functions and the labeling of non-terminal nodes. Then, Markov Models are used to recognize hierarchical syntactic structures. Each layer of a structure is represented by a separate Markov Model. The output of a lower layer is passed as input to a higher layer, hence the name: Cascaded Markov Models. Instead of simple symbols, the states emit partial context-free structures. The new techniques are applied to corpus annotation and partial parsing and are evaluated using corpora of different languages and domains.Ausgehend von Markov-Modellen, die für das Part-of-Speech-Tagging eingesetzt werden, stellt diese Arbeit Verfahren vor, die Markov-Modelle auch auf weiteren Ebenen der syntaktischen Verarbeitung erfolgreich nutzen. Dies betrifft zum einen Klassifikationen wie die Zuweisung grammatischer Funktionen und die Bestimmung von Kategorien nichtterminaler Knoten, zum anderen die Zuweisung hierarchischer, syntaktischer Strukturen durch Markov-Modelle. Letzteres geschieht durch die Repräsentation jeder Ebene einer syntaktischen Struktur durch ein eigenes Markov-Modell, was den Namen des Verfahrens prägt: Kaskadierte Markov-Modelle. Deren Zustände geben anstelle atomarer Symbole partielle kontextfreie Strukturen aus. Diese Verfahren kommen in der Korpusannotation und dem partiellen Parsing zum Einsatz und werden anhand mehrerer Korpora evaluiert

Unsupervised Syntactic Structure Induction in Natural Language Processing

This work addresses unsupervised chunking as a task for syntactic structure induction, which could help understand the linguistic structures of human languages especially, low-resource languages. In chunking, words of a sentence are grouped together into different phrases (also known as chunks) in a non-hierarchical fashion. Understanding text fundamentally requires finding noun and verb phrases, which makes unsupervised chunking an important step in several real-world applications. In this thesis, we establish several baselines and discuss our three-step knowledge transfer approach for unsupervised chunking. In the first step, we take advantage of state-of-the-art unsupervised parsers, and in the second, we heuristically induce chunk labels from them. We propose a simple heuristic that does not require any supervision of annotated grammar and generates reasonable (albeit noisy) chunks. In the third step, we design a hierarchical recurrent neural network (HRNN) that learns from these pseudo ground-truth labels. The HRNN explicitly models the composition of words into chunks and smooths out the noise from heuristically induced labels. Our HRNN a) maintains both word-level and phrase-level representations and b) explicitly handles the chunking decisions by providing autoregressiveness at each step. Furthermore, we make a case for exploring the self-supervised learning objectives for unsupervised chunking. Finally, we discuss our attempt to transfer knowledge from chunking back to parsing in an unsupervised setting. We conduct comprehensive experiments on three datasets: CoNLL-2000 (English), CoNLL-2003 (German), and the English Web Treebank. Results show that our HRNN improves upon the teacher model (Compound PCFG) in terms of both phrase F1 and tag accuracy. Our HRNN can smooth out the noise from induced chunk labels and accurately capture the chunking patterns. We evaluate different chunking heuristics and show that maximal left-branching performs the best, reinforcing the fact that left-branching structures indicate closely related words. We also present rigorous analysis on the HRNN's architecture and discuss the performance of vanilla recurrent neural networks

The role of language proficiency and statistical learning in on-line comprehension of syntax among bilingual adult readers

Statistical learning (SL) is the ability to identify co-occurring regularities from the environment, and has been implicated in learning across a range of skills, including language. This research project investigated whether there are associations between SL and on-line sentence processing in L1 Chinese L2 English bilinguals, and sought to examine whether second language proficiency mediated the relationship between visual SL and L2 language processing. To this end, two studies were conducted. In Study 1, sixty Chinese-English bilinguals completed a self-paced reading task in Mandarin and English, which tested participants’ on-line processing of subject and object relative clauses (RCs). They also completed a nonlinguistic visual SL task and a battery of additional measures measuring L2 English proficiency and general cognitive abilities. The results revealed that only nonverbal intelligence predicted L1 Chinese RCs processing, and neither visual SL capacity nor L2 proficiency predicted L2 English RCs processing. One possible explanation is that SL is partially modality-specific. Therefore, an auditory SL task was employed in addition to visual SL task in Study 2. In Study 2, fifty-two native Mandarin-speaking adults completed tests of visual and auditory SL, a self-paced reading task measuring the online processing of Mandarin relative clauses, and measures of general cognitive abilities. The results showed that auditory SL capacity independently predicted reading times in the self-paced reading task. Visual SL was also related to language processing, although the effect was marginal. The findings from Study 2 suggest that individual differences in adults’ capacity for SL are associated with on-line processing of Chinese

Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

Modelling the developmental patterning of finiteness marking in English, Dutch, German and Spanish using MOSAIC

In this paper we apply MOSAIC (Model of Syntax Acquisition in Children) to the simulation of the developmental patterning of children’s Optional Infinitive (OI) errors in four languages: English, Dutch, German and Spanish. MOSAIC, which has already simulated this phenomenon in Dutch and English, now implements a learning mechanism that better reflects the theoretical assumptions underlying it, as well as a chunking mechanism which results in frequent phrases being treated as one unit. Using one, identical model that learns from child-directed speech, we obtain a close quantitative fit to the data from all four languages, despite there being considerable cross-linguistic and developmental variation in the OI phenomenon. MOSAIC successfully simulates the difference between Spanish (a pro-drop language where OI errors are virtually absent), and Obligatory Subject languages that do display the OI phenomenon. It also highlights differences in the OI phenomenon across German and Dutch, two closely related languages whose grammar is virtually identical with respect to the relation between finiteness and verb placement. Taken together, these results suggest that (a) cross-linguistic differences in the rates at which children produce Optional Infinitives are graded, quantitative differences that closely reflect the statistical properties of the input they are exposed to and (b) theories of syntax acquisition need to consider more closely the role of input characteristics as determinants of quantitative differences in the cross-linguistic patterning of phenomena in language acquisition