Italian VerbNet: A Construction based Approach to Italian Verb Classification

This paper proposes a new method for Italian verb classification -and a preliminary example of resulting classes- inspired by Levin (1993) and VerbNet (Kipper-Schuler, 2005), yet partially independent from these resources; we achieved such a result by integrating Levin and VerbNet’s models of classification with other theoretic frameworks and resources. The classification is rooted in the constructionist framework (Goldberg, 1995; 2006) and is distribution-based. It is also semantically characterized by a link to FrameNet’ssemanticframesto represent the event expressed by a class. However, the new Italian classes maintain the hierarchic “tree” structure and monotonic nature of VerbNet’s classes, and, where possible, the original names (e.g.: Verbs of Killing, Verbs of Putting, etc.). We therefore propose here a taxonomy compatible with VerbNet but at the same time adapted to Italian syntax and semantics. It also addresses a number of problems intrinsic to the original classifications, such as the role of argument alternations, here regarded simply as epiphenomena, consistently with the constructionist approach

FrameNet's Frames vs. Levin's Verb Classes

The classification of verbs in Levin's (1993) English Verb Classes and Alternations: A preliminary Investigation, on the basis of both intuitive semantic grouping and their participation in valence alternations, is often used by the NLP community as evidence of the semantic similarity of verbs (Jing & McKeown 1998; Lapata & Brew 1999; Kohl et al. 1998). In this paper, we compare the Levin classification with the work of the FrameNet project (Fillmore & Baker 2001), where words (not just verbs) are grouped according to the conceptual structures (frames) that underlie them and their combinatorial patterns are inductively derived from corpus evidence. This means that verbs grouped together in FrameNet (FN) might be semantically similar but have different (or no) alternations, and that verbs which share the same alternation might be represented in two different semantic frames

Combining Multiple, Large-Scale Resources in a Reusable Lexicon for Natural Language Generation

A lexicon is an essential component in a generation system but few efforts have been made to build a rich, large-scale lexicon and make it reusable for different generation applications. In this paper, we describe our work to build such a lexicon by combining multiple, heterogeneous linguistic resources which have been developed for other purposes. Novel transformation and integration of resources is required to reuse them for generation. We also applied the lexicon to the lexical choice and realization com- ponent of a practical generation application by using a multi-level feedback architecture. The integration of the lexicon and the architecture is able to effectively improve the system paraphrasing power, minimize the chance of grammatical errors, and simplify the development process substantially

Combining Multiple, Large-Scale Resources in a Reusable Lexicon for Natural Language Generation

A lexicon is an essential component in a gener-ation system but few efforts have been made to buiht a rich, large-scale lexicon and make it reusable for different generation applications. In this paper, we describe our work to build 'inch a lexicon by combining multiple, heteroge-neous linguistic resources which have been de-veloped for other purposes. Novel transforma-tion and integration of resources is required to reuse them for generation. We also applied the lexicon to the lexical choice and realization com-ponent of a practical generation application by using a multi-level feedback architecture. The integration of the lexicon and the architecture is able to effectively improve the system para-phrasing power, minimize the chance of gram-matical errors, and simplify the development process substantially.

Italian VerbNet: A Construction-based Approach to Italian Verb Classification

L'elaborato consiste nella proposta di una nuova classificazione verbale per l'italiano, sulla base dell'autorevole modello inglese di VerbNet. Il metodo elaborato, punto centrale della ricerca, è stato sviluppato in modo da consentire la creazione di classi compatibili con il modello inglese, ma allo stesso tempo autonome e basate su criteri teorici indipendenti. Ad una parte esplicativa segue l'esposizione dei dati correlati da commenti

Unsupervised Graph-Based Similarity Learning Using Heterogeneous Features.

Relational data refers to data that contains explicit relations among objects. Nowadays, relational data are universal and have a broad appeal in many different application domains. The problem of estimating similarity between objects is a core requirement for many standard Machine Learning (ML), Natural Language Processing (NLP) and Information Retrieval (IR) problems such as clustering, classiffication, word sense disambiguation, etc. Traditional machine learning approaches represent the data using simple, concise representations such as feature vectors. While this works very well for homogeneous data, i.e, data with a single feature type such as text, it does not exploit the availability of dfferent feature types fully. For example, scientic publications have text, citations, authorship information, venue information. Each of the features can be used for estimating similarity. Representing such objects has been a key issue in efficient mining (Getoor and Taskar, 2007). In this thesis, we propose natural representations for relational data using multiple, connected layers of graphs; one for each feature type. Also, we propose novel algorithms for estimating similarity using multiple heterogeneous features. Also, we present novel algorithms for tasks like topic detection and music recommendation using the estimated similarity measure. We demonstrate superior performance of the proposed algorithms (root mean squared error of 24.81 on the Yahoo! KDD Music recommendation data set and classiffication accuracy of 88% on the ACL Anthology Network data set) over many of the state of the art algorithms, such as Latent Semantic Analysis (LSA), Multiple Kernel Learning (MKL) and spectral clustering and baselines on large, standard data sets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89824/1/mpradeep_1.pd

Identifying Roles in Social Networks using Linguistic Analysis.

Social media sites have been significantly growing in the past few years. This resulted in the emergence of several communities of communicating groups, and a huge amount of text exchanged between members of those groups. In our work, we study how linguistic analysis techniques can be used for understanding the implicit relations that develop in on-line communities. We use this understanding to develop models that explain the processes that govern language use and how it reveals the formation of social relations. We study the relation between language choices and attitude between participants and how they may lead to or reveal antagonisms and rifts in social groups. Both positive (friendly) and negative (antagonistic) relations exist between individuals in communicating communities. Negative relations have received very little attention, when compared to positive relations, because of the lack of an explicit notion of labeling negative relations in most social computing applications. We alleviate this problem by studying text exchanged between participants to mine their attitude. Another important aspect of our research is the study of influence in discussions and how it affects participants’ discourse. In any debate or discussion, there are certain types of persons who influence other people and affect their ideas and rhetoric. We rely on natural language processing techniques to find implicit connections between individuals that model this influence. We couple this with network analysis techniques for identifying the most authoritative or salient entities. We also study how salience evolves over time. Our work is uniquely characterized by combining linguistic features and network analysis to reveal social roles in different communities. The methods we developed can find several interesting areas of applications. For example, they can be used for identifying authoritative sources in social media, finding influential people in communities, mining attitude toward events and topics, detecting rifts and subgroup formation, summarizing different viewpoints with respect to some topic or entity, and many other such applications.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86271/1/hassanam_1.pd