E nglish- T urkish Parallel Semantic Annotation of P enn- T reebank (2019)

Abstract: This paper reports our efforts in constructing a sense-labeled English-Turkish parallel corpus using the traditional method of manual tagging. We tagged a pre-built parallel treebank which was translated from the Penn Treebank corpus. This approach allowed us to generate a resource combining syntactic and semantic information. We provide statistics about the corpus itself as well as information regarding its development process.

## introduction parallel corpora, which are a collection of texts in one language and their translations in at least one other, can be used in a variety of fields, such as translation studies and contrastive linguistics. they are used for many different purposes including creating new linguistic resources such as lexicons and wordnet @xcite . as for the relationship between parallel corpora and natural language processing (nlp) studies, in addition to the fact nlp studies use parallel corpora as material bases or testing arenas, nlp studies also contribute to the development of corpora in many areas, especially in corpus annotation. in this paper, we present a sense-tagged english-turkish parallel corpus, which is the only corpus for the english-turkish combination having both semantic and syntactic information. it has been built on the preceding parallel treebank construction and morphological analysis efforts reported in @xcite and @xcite . the aim of this study is to investigate the possibility of a parallel semantic annotation for an english-turkish corpus. the motivation behind the study is the potential contribution of this parallel semantic annotation to several nlp tasks such as automatic annotation, statistical machine translation and word sense disambiguation. this paper is organized as follows: we give background information about lexical semantics in section 2 and present the related work in section 3. the details of our corpus and how it is constructed are given in section 4. we provide the annotation statistics about the corpus in section 5 and conclude in section 6. ## lexical semantics in linguistics, lexical semantics is the study of word meaning. the main challenge in this field is generated from 'polysemy', which is the term used for the phenomenon of a single orthographic word having multiple, interrelated senses. in classical dictionaries, these senses are listed under a single lexical entry and, as stated in @xcite , "you shall know a word by the company it keeps", that is, only with the help of the context one can pin down the particular sense in which a word is used. a further challenge in the field stems from collations, i.e. groups of words having "a unitary meaning which does not correspond to the compositional meaning of their parts" @xcite . hence, as far as compositionality is considered to be crucial to semantic analysis, there are two central concerns for the semanticist: (i) at the lexical level, choosing the correct sense of a given word within a context, and (ii) at the sentence level, determining how a particular combination of words should be interpreted. languages also differ in terms of how lexical items are combined, which is directly related to how compositionality is to be interpreted. therefore, the success and adequacy of a multi-lingual semantic analysis not only requires taking "into consideration the multitude of different senses of words across languages", but also "effective mechanisms that allow for the linking of extended word senses in diverging polysemy patterns" @xcite . when it comes to interlingual semantics studies, even further complications arise. for one, there is a huge discrepancy between languages in terms of which semantic components they lexicalize. for instance, in analytic languages like english, functional morphemes are free forms, such as determiners and appositions, whereas in agglutinative languages, such as turkish, syntactic relations are expressed mainly via affixation. hence, a single orthographic word in turkish may correspond to a phrase consisting of a combination of multiple free morphemes in english. ## related work in this section, we present previous work and provide a comparison of our corpus with other corpora mainly with reference to their sense annotation process and the number of annotated words. ## corpus in this section, we describe how the data in our corpus were extracted and organized, give details of our annotation tool, explain how the data in both turkish and english partitions were annotated, give an account of our data format, and finally, evaluate our annotation. ## conclusion in this paper, we reported our experience on manual tagging of english and turkish senses in an english-turkish parallel treebank, which had been parsed and enhanced with morphological features before the semantic annotation process. our study has shown that it is possible to perform a parallel semantic annotation for an english-turkish corpus and that the pre-processing stage for the parsing and morphological enhancement has been useful as it has accelerated the sense annotation process by providing the annotators with shorter lists of senses of a word in a given sentence. as a future work, we plan to expand the size of the corpus by following the same manner of procedure, perform word sense disambiguation experiments on it with various classifiers and feature sets and make use of our parallel corpora in various nlp tasks including automatic annotation, statistical machine translation or word sense disambiguation.