14.1878, Diss: Comp Ling: Schulte: 'Experiments on the...'

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LINGUIST List:  Vol-14-1878. Mon Jul 7 2003. ISSN: 1068-4875.

Subject: 14.1878, Diss: Comp Ling: Schulte: 'Experiments on the...'

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1)
Date:  Mon, 07 Jul 2003 06:20:31 +0000
From:  schulte at ims.uni-stuttgart.de
Subject:  Experiments on the Automatic Induction...

-------------------------------- Message 1 -------------------------------

Date:  Mon, 07 Jul 2003 06:20:31 +0000
From:  schulte at ims.uni-stuttgart.de
Subject:  Experiments on the Automatic Induction...

Institution: University of Stuttgart
Program: Institute for Natural Language Processing (IMS)
Dissertation Status: Completed
Degree Date: 2003

Author: Sabine  Schulte im Walde

Dissertation Title: Experiments on the Automatic Induction of German
Semantic Verb Classes

Dissertation URL: http://www.ims.uni-stuttgart.de/~schulte/PhD-Thesis.html

Linguistic Field: Computational Linguistics

Subject Language: German, Standard (code: GER)

Dissertation Director 1: Hans Kamp
Dissertation Director 2: Chris Brew

Dissertation Abstract:

This thesis investigates the potential and the limits of an automatic
acquisition of semantic classes for German verbs. Semantic verb
classes are an artificial construct of natural language which
generalises over verbs according to their semantic properties; the
class labels refer to the common semantic properties of the verbs in a
class at a general conceptual level, and the idiosyncratic lexical
semantic properties of the verbs are either added to the class
description or left underspecified. Examples for conceptual structures
are `Position' verbs such as `liegen' (to lie), `sitzen' (to sit),
`stehen' (to stand). On the one hand, verb classes reduce redundancy
in verb descriptions, since they encode the common properties of
verbs. On the other hand, verb classes can predict and refine
properties of a verb that received insufficient empirical evidence,
with reference to verbs in the same class; under this aspect, a verb
classification is especially useful for the pervasive problem of data
sparseness in NLP, where little or no knowledge is provided for rare
events. To my knowledge, no German verb classification is available
for NLP applications. Such a classification would therefore provide a
principled basis for filling a gap in available lexical knowledge.

The construction of semantic classes typically benefits from a
long-standing linguistic hypothesis which asserts a tight connection
between the lexical meaning of a verb and its behaviour, cf.
Levin (1993). We can utilise this meaning-behaviour relationship
in that we induce a verb classification on basis of verb features
describing verb behaviour (which are easier to obtain automatically
than semantic features) and expect the resulting
behaviour-classification to agree with a semantic classification to a
certain extent. A common approach to define verb behaviour is captured
by the diathesis alternation of verbs. I have developed, implemented
and trained a statistical grammar model for German which provides
empirical lexical information, specialising on but not restricted to
the subcategorisation behaviour of verbs. The grammar model serves as
source for a German verb description at the syntax-semantic interface:
The verbs are distributionally described on three levels, each of them
refining the previous level by additional information. The first level
encodes a purely syntactic definition of verb subcategorisation, the
second level encodes a syntactico-semantic definition of
subcategorisation with prepositional preferences, and the third level
encodes a syntactico-semantic definition of subcategorisation with
prepositional and selectional preferences. The most elaborated
description comes close to a definition of verb alternation behaviour.

The automatic induction of the German verb classes is performed by the
k-Means algorithm, a standard unsupervised clustering technique as
proposed by Forgy (1965). The algorithm uses the
syntactico-semantic descriptions of the verbs as empirical verb
properties and learns to induce a semantic classification from this
input data. The clustering outcome cannot be a perfect semantic verb
classification, since (i) the meaning-behaviour relationship on which
we rely for the clustering is not perfect, and (ii) the clustering
method is not perfect for the ambiguous verb data. But the goal of
this thesis is not necessarily to obtain the optimal clustering
result, but to understand the potential and the restrictions of the
natural language clustering approach. Only in this way we can develop
a methodology which can be applied to large-scale data. Key issues of
the clustering methodology refer to linguistic aspects on the one
hand, and to technical aspects on the other hand.

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LINGUIST List: Vol-14-1878