On the Establishment of Database of Morpheme Meaning Annotations for Modern Chinese Compounds

This research establishes Database of Morpheme Meaning Annotations for Compounds, and statistically analyses the using frequency of morphemes and their meanings in the database. The research shows that the using frequency of “zi” (子) and “er” (儿) is the highest, and their meanings with the highest using frequency are applied when the morphemes are affixes. Among various meanings of one morpheme, only one meaning would be applied with high frequency, with simpler and specific implications. By overviewing all morphemes, it is observed that those with higher using frequency do not necessarily have meanings with high frequency of use. By analyzing internal structures of compounds, this paper finds that noun-modification compounds are the most, followed with verb-object compounds and affixation compounds. The semantic logic relations between morphemes and compound words can be: word definitions can be the abstraction of objects signified by morpheme meanings, or of senses signified by morpheme meanings, or of spatial meanings signified by morpheme meanings.

Definition as a Logical and Linguistic Category

Definition is a logical and linguistic category that for a long time has been the subject of logicians’ and philosophers’ research. Today we understand a definition as a logical method that allows us to discover a word’s content, i.e. what it means in everyday use or what a speaker intends it to mean for this speaker’s own specific purposes. A definition consists of two parts: definiendum (that what is defined) and definiens (that which defines). The definiendum refers to the exact object, action, state etc., that is to be defined. The definiens contains the information necessary to define this object, action, state etc. This information is obtained during the process of definition. In the language, a definition can mean both the process of developing a sentence and the result of this process, i.e. the sentence itself. When composing an explanation of a word’s lexical meaning, we should draw on the vast experience of Russian lexicography. Practical lexicography gave us a wide range of kinds of word definitions which are now being extensively researched as theoretical generalizations and conclusions. This article explores methods of explanation of word meanings in the context of different possible logical relations between definiendum and definiens: inclusion, overlap, complementarity, adjacency. The existing word definitions (hyponymic, identifying, enumerative, synonymic, antonymic) can be distributed between those kinds of logical relations. Descriptive explanations are regarded as a specific kind of definition. Theoretical generalizations and conclusions are backed by examples from the main explanatory dictionaries of the Russian language.The conclusion is drawn that parts of a word definition can enter into different kinds of logical relations. Exploration of those relations is highly important for educational purposes as methodological basis for correct definitions of word meanings as well as for the composition of lexicographic explanations. At the same time, we shouldn’t forget that there are various ways to define a word’s lexical meaning. One and the same word can be defined in a number of ways, so the lexicographers’ main task is still to choose the best way or combination of ways that can fully and accurately discover the meaning of the word being defined.

Sentiment polarity classification of tweets using a extended dictionary

With the purpose of classifying text based on its sentiment polarity (positive or negative), we proposed an extension of a 68,000 tweets corpus through the inclusion of word definitions from a dictionary of the Real Academia Espa\~{n}ola de la Lengua (RAE). A set of 28,000 combinations of 6 Word2Vec and support vector machine parameters were considered in order to evaluate how positively would affect the inclusion of a RAE's dictionary definitions classification performance. We found that such a corpus extension significantly improve the classification accuracy. Therefore, we conclude that the inclusion of a RAE's dictionary increases the semantic relations learned by Word2Vec allowing a better classification accuracy.

Subjective Lexical Characteristics: Comparing Ratings of Members of the Target Population and Doctors for Words Stemming from a Medical Context

The present study investigated the subjective lexical characteristics of words stemming from a medical context by comparing estimations of the target population (age range = 46–89) and of doctors. A total of 58 members of the target population and 22 oncologists completed measures of subjective frequency and emotional valence for words previously collected in interviews of announcement of cancer diagnosis. The members of the target population also completed tests of word definitions, without and within context. As expected, most of the words were rated less familiar, more negative and as generating more intense emotions to the target population than to the doctors. Moreover, only a few words were correctly defined by the target population. Adding a context helped the participants to define most of the words correctly. Importantly, we identified words that were rated familiar by the patients although they did not know their exact meaning. Overall, these results highlight the importance of taking into account the subjective lexical characteristics of words used in specific contexts.