Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation

In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun--noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.Comment: EMNLP 2018: Conference on Empirical Methods in Natural Language Processing (EMNLP

Opinion Holder and Target Extraction on Opinion Compounds – A Linguistic Approach

We present an approach to the new task of opinion holder and target extraction on opinion compounds. Opinion compounds (e.g. user rating or victim support) are noun compounds whose head is an opinion noun. We do not only examine features known to be effective for noun compound analysis, such as paraphrases and semantic classes of heads and modifiers, but also propose novel features tailored to this new task. Among them, we examine paraphrases that jointly consider holders and targets, a verb detour in which noun heads are replaced by related verbs, a global head constraint allowing inferencing between different compounds, and the categorization of the sentiment view that the head conveys

Klusteroinnin Hyödyntäminen Suomalaisten Yritysten Toimialaluokittelussa

An industrial classification system is a set of classes meant to describe different areas of business. Finnish companies are required to declare one main industrial class from TOL 2008 industrial classification system. However, the TOL 2008 system is designed by the Finnish authorities and does not serve the versatile business needs of the private sector. The problem was discovered in Alma Talent Oy, the commissioner of the thesis. This thesis follows the design science approach to create new industrial classifications. To find out what is the problem with TOL 2008 indus- trial classifications, qualitative interviews with customers were carried out. Interviews revealed several needs for new industrial classifications. According to the customer interviews conducted, classifications should be 1) more detailed, 2) simpler, 3) updated regularly, 4) multi-class and 5) able to correct wrongly assigned TOL classes. To create new industrial classifications, un- supervised natural language processing techniques (clustering) were tested on Finnish natural language data sets extracted from company websites. The largest data set contained websites of 805 Finnish companies. The experiment revealed that the interactive clustering method was able to find meaningful clusters for 62%-76% of samples, depending on the clustering method used. Finally, the found clusters were evaluated based on the requirements set by customer interviews. The number of classes extracted from the data set was significantly lower than the number of distinct TOL 2008 classes in the data set. Results indicate that the industrial classification system created with clustering would contain significantly fewer classes compared to TOL 2008 industrial classifications. Also, the system could be updated regularly and it could be able to correct wrongly assigned TOL classes. Therefore, interactive clustering was able to satisfy three of the five requirements found in customer interviews

The Use of Story Reading as a Method of Improving Verbal Expression of Head Start Children

The purpose of this experimental study was to conduct and evaluate a teaching method for improving verbal expression performance of Head Start children. The teaching method of language stimulation given the experimental subjects was based on story reading and retelling with active participation by the children in daily small group tutoring sessions, for seven weeks. An academic program given the control subjects included specific vocabulary and sequencing training. Verbal expression was measured by an analysis of stories told by each subject before and after tutoring, in response to sequence pictures and standup figures. Measures of vocabulary, sentence structure and evidence of sequence were used in the analysis. The experimental language tutored group gained significantly from pre- to posttest in 11 Of 20 verbal expression criteria. Although a comparison of group means showed the experimental group\u27s performance to have exceeded that of the control group in 15 criteria, only one vocabulary score was significantly greater for the experimental subjects. It was concluded that verbal expression skills can be accelerated through training. The teaching method based on story reading was recommended for use by Odgen Head Start teachers as one method of improving verbal expression

Topic identification using filtering and rule generation algorithm for textual document

Information stored digitally in text documents are seldom arranged according to specific topics. The necessity to read whole documents is time-consuming and decreases the interest for searching information. Most existing topic identification methods depend on occurrence of terms in the text. However, not all frequent occurrence terms are relevant. The term extraction phase in topic identification method has resulted in extracted terms that might have similar meaning which is known as synonymy problem. Filtering and rule generation algorithms are introduced in this study to identify topic in textual documents. The proposed filtering algorithm (PFA) will extract the most relevant terms from text and solve synonym roblem amongst the extracted terms. The rule generation algorithm (TopId) is proposed to identify topic for each verse based on the extracted terms. The PFA will process and filter each sentence based on nouns and predefined keywords to produce suitable terms for the topic. Rules are then generated from the extracted terms using the rule-based classifier. An experimental design was performed on 224 English translated Quran verses which are related to female issues. Topics identified by both TopId and Rough Set technique were compared and later verified by experts. PFA has successfully extracted more relevant terms compared to other filtering techniques. TopId has identified topics that are closer to the topics from experts with an accuracy of 70%. The proposed algorithms were able to extract relevant terms without losing important terms and identify topic in the verse

Knowledge-based methods for automatic extraction of domain-specific ontologies

Semantic web technology aims at developing methodologies for representing large amount of knowledge in web accessible form. The semantics of knowledge should be easy to interpret and understand by computer programs, so that sharing and utilizing knowledge across the Web would be possible. Domain specific ontologies form the basis for knowledge representation in the semantic web. Research on automated development of ontologies from texts has become increasingly important because manual construction of ontologies is labor intensive and costly, and, at the same time, large amount of texts for individual domains is already available in electronic form. However, automatic extraction of domain specific ontologies is challenging due to the unstructured nature of texts and inherent semantic ambiguities in natural language. Moreover, the large size of texts to be processed renders full-fledged natural language processing methods infeasible. In this dissertation, we develop a set of knowledge-based techniques for automatic extraction of ontological components (concepts, taxonomic and non-taxonomic relations) from domain texts. The proposed methods combine information retrieval metrics, lexical knowledge-base(like WordNet), machine learning techniques, heuristics, and statistical approaches to meet the challenge of the task. These methods are domain-independent and automatic approaches. For extraction of concepts, the proposed WNSCA+{PE, POP} method utilizes the lexical knowledge base WordNet to improve precision and recall over the traditional information retrieval metrics. A WordNet-based approach, the compound term heuristic, and a supervised learning approach are developed for taxonomy extraction. We also developed a weighted word-sense disambiguation method for use with the WordNet-based approach. An unsupervised approach using log-likelihood ratios is proposed for extracting non-taxonomic relations. Further more, a supervised approach is investigated to learn the semantic constraints for identifying relations from prepositional phrases. The proposed methods are validated by experiments with the Electronic Voting and the Tender Offers, Mergers, and Acquisitions domain corpus. Experimental results and comparisons with some existing approaches clearly indicate the superiority of our methods. In summary, a good combination of information retrieval, lexical knowledge base, statistics and machine learning methods in this study has led to the techniques efficient and effective for extracting ontological components automatically