Aligning parallell terms in a parallell corpus can be done by aligning all words and phrases in the corpus and then performing term extraction on the aligned set of word pairs. Alternatively, term extraction in the source and target text can be made separately and then the resulting term candidates can be aligned, forming aligned parallell terms. This thesis describes an implementation of a word aligner that is applied on extracted term candidates in both the source and the target texts. The term aligner uses statistical measures, the tool Giza++ and heuristics in the search for alignments. The evaluation reveals that the best results are obtained when the term alignment relies heavily on the Giza++ tool and Levenshtein heuristic..

Aligning parallell terms in a parallell corpus can be done by aligning all words and phrases in the corpus and then performing term extraction on the aligned set of word pairs. Alternatively, term extraction in the source and target text can be made separately and then the resulting term candidates can be aligned, forming aligned parallell terms. This thesis describes an implementation of a word aligner that is applied on extracted term candidates in both the source and the target texts. The term aligner uses statistical measures, the tool Giza++ and heuristics in the search for alignments. The evaluation reveals that the best results are obtained when the term alignment relies heavily on the Giza++ tool and Levenshtein heuristic..

Statistiska Centralbyrån (SCB) samlar in data i form av enkätundersökningar. Dessa data måste sedan kodas med olika klassifikationer för att kunna användas i statistikproduktion. En sådan klassifikation är Socioekonomisk indelning (SEI).Denna rapport behandlar hur man automatiskt kan SEI-koda sådana enkätundersökningar så att varje enkät tilldelas en SEI-kod. SCB:s nuvarande algoritm kodar ca 50% av enkäterna och bygger på att det yrke som är angivet i enkäten finns med i ett lexikon. Om yrket inte finns med kan enkäten inte kodas.