Sökning: "Dependency Parsing"

Visar resultat 1 - 5 av 29 uppsatser innehållade orden Dependency Parsing.

1. 1. Syntax-based Concept Alignment for Machine Translation

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Arianna Masciolini; [2023-03-30]
   Nyckelord :computational linguistic; machine translation; concept alignment; syntax; dependency parsing; Universal Dependencies; Grammatical Framework;

   Sammanfattning : This thesis presents a syntax-based approach to Concept Alignment (CA), the task of finding semantical correspondences between parts of multilingual parallel texts, with a focus on Machine Translation (MT). Two variants of CA are taken into account: Concept Extraction (CE), whose aim is to identify new concepts by means of mere linguistic comparison, and Concept Propagation (CP), which consists in looking for the translation equivalents of a set of known concepts in a new language. LÄS MER

3. 2. Parallel Query Systems : Demand-Driven Incremental Compilers

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Christofer Nolander; [2023]
   Nyckelord :Query Systems; Parallelism; Incremental Computation; Compiler Architecture; Dependency Tracking; Query system; Parallelism; inkrementella beräkningar; kompilatorer; beroende spårning;

   Sammanfattning : Query systems were recently introduced as an architecture for constructing compilers, and have shown to enable fast and efficient incremental compilation, where results from previous builds is reused to accelerate future builds. With this architecture, a compiler is composed of several queries, each of which extracts a small piece of information about the source program. LÄS MER

4. 3. Cross-Lingual and Genre-Supervised Parsing and Tagging for Low-Resource Spoken Data

   Master-uppsats, Uppsala universitet/Institutionen för lingvistik och filologi

   Författare :Iliana Fosteri; [2023]
   Nyckelord :dependency parsing; part-of-speech tagging; low-resource languages; transcribed speech; large language models; cross-lingual learning; transfer learning; multi-task learning; Universal Dependencies;

   Sammanfattning : Dealing with low-resource languages is a challenging task, because of the absence of sufficient data to train machine-learning models to make predictions on these languages. One way to deal with this problem is to use data from higher-resource languages, which enables the transfer of learning from these languages to the low-resource target ones. LÄS MER

5. 4. Aspektbaserad Sentimentanalys för Business Intelligence inom E-handeln

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Albin Eriksson; Anton Mauritzon; [2022]
   Nyckelord :Aspect-based sentiment analysis; Aspect extraction; BERT; Business Intelligence; Dependency parsing; Ecommerce; KB-BERT; Sentiment analysis; Sentiment classification; Aspektbaserad sentimentanalys; Aspektextraktion; BERT; Business Intelligence; Dependensparsning; E-handel; KB-BERT; Sentimentanalys; Sentimentklassificering;

   Sammanfattning : Many companies strive to make data-driven decisions. To achieve this, they need to explore new tools for Business Intelligence. The aim of this study was to examine the performance and usability of aspect-based sentiment analysis as a tool for Business Intelligence in E-commerce. LÄS MER

7. 5. Syntactic Crossroads: Testing L2 sensitivity to Strong Crossover in an online experiment.

   Master-uppsats, Stockholms universitet/Institutionen för svenska och flerspråkighet

   Författare :Daniele Tucciarone; [2022]
   Nyckelord :;

   Sammanfattning : We investigated sensitivity of non-native speakers of English to the Strong Crossover (SCO) constraint and Binding Principle C. Taking Clahsen & Felser’s (2006) Shallow Structure Hypothesis as a theoretical foundation, we tested whether non-native speakers would show a similar ability in predictively processing syntactic gaps in Strong Crossover configurations as had English native speakers in Kush et al. LÄS MER