Temporal relation classification is one of the most challenging areas of natural language processing. Advances in this area have direct relevance to improving practical applications, such as question-answering and summarization systems, as well as informing theoretical understanding of temporal meaning realization in language. With the development of annotated textual materials, this domain is now accessible to empirical machine-learning oriented approaches, where systems treat temporal relation processing as a classification problem: i.e. a decision as per which label (before, after, identity, etc) to assign to a pair (i, j) of event indices in a text. Most reported systems in this new research domain utilize classifiers that make decisions effectively in isolation, without explicitly utilizing the decisions made about other indices in a document. In this work, we present a new strategy for temporal relation classification that utilizes global models of temporal relations in a document, choosing the optimal classification for all pairs of indices in a document subject to global constraints which may be linguistically motivated. We propose and evaluate two applications of global models to temporal semantic processing: joint prediction of situation entities with temporal relations, and temporal relations prediction guided by global coherence constraints. / text

This master thesis investigated how well a multiclass support vector machine approach is at classifying a fixed number of interpersonal relations between extracted entities of people from Swedish verdicts. With the help of manually tagged extracted pairs of people entities called relations, a multiclass support vector machine was used to train and test the performance of the classification. Different features and parameters were tested to optimize the method, and for the final experiment, a micro precision and recall of 91.75% were found. For macro precision and recall, the result was 73.29% and 69.29% respectively. This resulted in an macro F score of 71.23% and micro F score of 91.75%. The results showed that the method worked for a few of the relation classes, but more balanced data would have been needed to answer the research question to a full extent. / Detta examensarbete utforskade hur bra en multiklass stödvektor- maskin är på att klassificera sociala relationer mellan extraherade personentiteter ur svenska domar. Med hjälp av manuellt taggade par av personentiteter kallade relationer, har en multiklass stödvektormaskin tränats och testats på att klassifiera dessa relationer. Olika attribut och parametrar har testats för att optimera metoden, och för det slutgiltiga exprimentet har ett resultat på 91.75% för båda mikro precision och återkallning beräknats. För makro precision och återkallning har ett resultat på 73.29% respektive 69.29% beräknats. Detta resulterade i ett makro F värde på 71.23% och ett mikro F värde på 91.75%. Resultaten visade att metoden fungerade för några av relationsklasserna men mer balanserat data skulle ha behövts för att forskningsfrågan skulle kunna besvara helt.

Many approaches to solving tasks in the field of Natural Language Processing (NLP) use syntactic dependency trees (SDTs) as a feature to represent the latent nonlinear structure within sentences. Recently, work in parsing sentences to graph-based structures which encode semantic relationships between words—called semantic dependency graphs (SDGs)—has gained interest. This thesis seeks to explore the use of SDGs in place of and alongside SDTs within a relation classification system based on long short-term memory (LSTM) neural networks. Two methods for handling the information in these graphs are presented and compared between two SDG formalisms. Three new relation extraction system architectures have been created based on these methods and are compared to a recent state-of-the-art LSTM-based system, showing comparable results when semantic dependencies are used to enhance syntactic dependencies, but with significantly fewer training parameters.