Creating large semantic lexical resources for the Finnish language

Finnish belongs into the Finno-Ugric language family, and it is spoken by the vast majority of the people living in Finland. The motivation for this thesis is to contribute to the development of a semantic tagger for Finnish. This tool is a parallel of the English Semantic Tagger which has been developed at the University Centre for Computer Corpus Research on Language (UCREL) at Lancaster University since the beginning of the 1990s and which has over the years proven to be a very powerful tool in automatic semantic analysis of English spoken and written data. The English Semantic Tagger has various successful applications in the fields of natural language processing and corpus linguistics, and new application areas emerge all the time. The semantic lexical resources that I have created in this thesis provide the knowledge base for the Finnish Semantic Tagger. My main contributions are the lexical resources themselves, along with a set of methods and guidelines for their creation and expansion as a general language resource and as tailored for domain-specific applications. Furthermore, I propose and carry out several methods for evaluating semantic lexical resources. In addition to the English Semantic Tagger, which was developed first, and the Finnish Semantic Tagger second, equivalent semantic taggers have now been developed for Czech, Chinese, Dutch, French, Italian, Malay, Portuguese, Russian, Spanish, Urdu, and Welsh. All these semantic taggers taken together form a program framework called the UCREL Semantic Analysis System (USAS) which enables the development of not only monolingual but also various types of multilingual applications. Large-scale semantic lexical resources designed for Finnish using semantic fields as the organizing principle have not been attempted previously. Thus, the Finnish semantic lexicons created in this thesis are a unique and novel resource. The lexical coverage on the test corpora containing general modern standard Finnish, which has been the focus of the lexicon development, ranges from 94.58% to 97.91%. However, the results are also very promising in the analysis of domain-specific text (95.36%), older Finnish text (92.11–93.05%), and Internet discussions (91.97–94.14%). The results of the evaluation of lexical coverage are comparable to the results obtained with the English equivalents and thus indicate that the Finnish semantic lexical resources indeed cover the majority of core Finnish vocabulary

Thematic Annotation: extracting concepts out of documents

Contrarily to standard approaches to topic annotation, the technique used in this work does not centrally rely on some sort of -- possibly statistical -- keyword extraction. In fact, the proposed annotation algorithm uses a large scale semantic database -- the EDR Electronic Dictionary -- that provides a concept hierarchy based on hyponym and hypernym relations. This concept hierarchy is used to generate a synthetic representation of the document by aggregating the words present in topically homogeneous document segments into a set of concepts best preserving the document's content. This new extraction technique uses an unexplored approach to topic selection. Instead of using semantic similarity measures based on a semantic resource, the later is processed to extract the part of the conceptual hierarchy relevant to the document content. Then this conceptual hierarchy is searched to extract the most relevant set of concepts to represent the topics discussed in the document. Notice that this algorithm is able to extract generic concepts that are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure

Towards Universal Semantic Tagging

The paper proposes the task of universal semantic tagging---tagging word tokens with language-neutral, semantically informative tags. We argue that the task, with its independent nature, contributes to better semantic analysis for wide-coverage multilingual text. We present the initial version of the semantic tagset and show that (a) the tags provide semantically fine-grained information, and (b) they are suitable for cross-lingual semantic parsing. An application of the semantic tagging in the Parallel Meaning Bank supports both of these points as the tags contribute to formal lexical semantics and their cross-lingual projection. As a part of the application, we annotate a small corpus with the semantic tags and present new baseline result for universal semantic tagging.Comment: 9 pages, International Conference on Computational Semantics (IWCS

A Deep Network Model for Paraphrase Detection in Short Text Messages

This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whether they are semantically identical. An important insight from this work is that existing paraphrase systems perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts. Challenges with paraphrase detection on user generated short texts, such as Twitter, include language irregularity and noise. To cope with these challenges, we propose a novel deep neural network-based approach that relies on coarse-grained sentence modeling using a convolutional neural network and a long short-term memory model, combined with a specific fine-grained word-level similarity matching model. Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus

Challenges and solutions for Latin named entity recognition

Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English. Data sparsity in Latin presents a number of challenges for traditional Named Entity Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90% F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality

The interaction of knowledge sources in word sense disambiguation

Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results. We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus.Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems