Lexical semantics

Li, Jianguo and Chris Brew. (2007) Disambiguating Levin Verbs without Using Hand-tagged Data Recent Advances in Natural Language Processing September 27-29, 2007, Borovets, Bulgaria

Li, Jianguo and Chris Brew. (2006) Parsing and Subcategorization Data In Proceedings of the COLING/ACL 2006,515–522 Sydney, Australia

Li, Jianguo, Chris Brew and Eric Fosler-Lussier 2005 Robust Extraction of Subcategorization Data from Spoken Language International Workshop on Parsing Technologies Vancouver, Canada

Li, Jianguo and Chris Brew. (2005) Automatic extraction of subcategorisation frames from spoken corpora In Katrin Erk,Alissa Melinger and Sabine Schulte im Walde Interdisciplinary Workshop on the Identification and Representation of Verb Features and Verb Classes Saarland University, Germany

Lapata, Mirella and Chris Brew (2004) Verb Class Disambiguation using Informative Priors Computational Linguistics: (3)0:1, 45-73

Brew, Chris and Sabine Schulte im Walde. (2002). Spectral Clustering for German Verbs. In Haji ˇc and Matsumoto Proceedings of the 7th Conference on Empirical Methods in Natural Language Processing, Philadelphia, PA., pages 117–124.

Schulte im Walde, Sabine and Chris Brew. (2002). Inducing German Semantic Verb Classes from Purely Syntactic Subcategorisation Information. In ACL Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia., pages 223–230.

Lapata, Maria and Chris Brew. (1999). Using subcategorization to resolve verb class ambiguity. In Pascale Fung and Joe Zhou, editors, Joint SIGDAT Conference on Empirical Methods in NLP and Very Large Corpora, pages 397–404, College Park, MD

Poesio, Massimo, Sabine Schulte im Walde, and Chris Brew. (1998). Lexical clustering and definite description interpretation. In J.Choi and N.Green, editors, AAAI Spring Symposium on Learning for Discourse Interpretation, pages 82–89, Stanford, March. American Association for Artificial Intelligence.

Ph.D Theses

1. Tim Weale (DoD, Fort Meade)
   1. Term Relatedness from Wiki-Based Resources Using Sourced PageRank
   2. This dissertation concerns itself with creating a new algorithm for automatically measuring the amount of relatedness between a given pair of terms. Research into term relatedness is important because it has been empirically demonstrated that using relatedness metrics can improve the performance of tasks in Natural Language Processing and Information Retrieval by expanding the usable vocabulary. Previous relatedness metrics have used a variety of sources of semantic data to judge term relatedness, including text corpora, expertly-constructed resources and, most recently, Wikipedia and Wiktionary. The primary focus of this dissertation is the creation of a new metric for deriving term relatedness from the graph structure of Wikipedia and Wiktionary using Sourced PageRank, a modified version of the PageRank algorithm, to generate the relatedness values.
2. Kirk Baker (iCubed analytics)
   1. MULTILINGUAL DISTRIBUTIONAL LEXICAL SIMILARITY
   2. This dissertation addresses the problem of learning an accurate and scalable lexical classifier in the absence of large amounts of hand-labeled training data. One approach to this problem involves using a rule-based system to generate large amounts of data that serve as training examples for a secondary lexical classifier. The viability of this approach is demonstrated for the task of automatically identifying English loanwords in Korean. A set of rules describing changes English words undergo when they are borrowed into Korean is used to generate training data for an etymological classification task. Although the quality of the rule-based output is low, on a sufficient scale it is reliable enough to train a classifier that is robust to the deficiencies of the original rule-based output and reaches a level of performance that has previously been obtained only with access to substantial hand-labeled training data. The second approach to the problem of obtaining labeled training data uses the output of a statistical parser to automatically generate lexical-syntactic co-occurrence features. These features are used to partition English verbs into lexical semantic classes, producing results on a substantially larger scale than any previously reported and yielding new insights into the properties of verbs that are responsible for their lexical categorization. The work here is geared towards automatically extending the coverage of verb classification schemes such as Levin, VerbNet, and FrameNet to other verbs that occur in a large text corpus.
3. Jianguo Li, Motorola
   1. HYBRID METHODS FOR ACQUISITION OF LEXICAL INFORMATION: THE CASE FOR VERBS
   2. Improved automatic text understanding requires detailed linguistic information about the words that comprise the text. Particularly crucial is the knowledge about predicates, typically verbs, which communicate both the event being expressed and how participants are related to the event. ... First, deriving Levin-style verb classifications from text corpora helps avoid the expensive hand-coding of such information, but appropriate features must be identified and demonstrated to be effective. One of our primary goals is to assess the linguistic conditions which are crucial for lexical classification of verbs. In particular, we experiment with different ways of mixing syntactic and lexical information for improved verb classification. Second, Levin verb classification provides a systematic account of verb polysemy. We propose a class-based method for disambiguating Levin verbs using only untagged data. The basic working hypothesis is that verbs in the same Levin class tend to share their subcategorization patterns as well as neighboring words. In practice, information about unambiguous verbs in a particular Levin class is employed to disambiguate the ambiguous ones in the same class. Last, automatically created verb classifications are likely to deviate from manually crafted ones, therefore it is of great importance to understand whether automatically created verb classifications can benefit the wider NLP community. We propose to integrate verb class information, automatically learned from text corpora, into a particular parsing task, PP-attachment disambiguation.
      1. Sabine Schulte im Walde Stuttgart
         1. Experiments on the Automatic Induction of German Semantic Verb Classes
            1. 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.