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TCL - a Lexicon of Turkish Discourse Connectives

It is known that discourse connectives are the most salient indicators of discourse relations. State-of-the-art parsers being developed to predict explicit discourse connectives exploit annotated discourse corpora but a lexicon of discourse connectives is also needed to enable further research in discourse structure and support the development of language technologies that use these structures for text understanding. This paper presents a lexicon of Turkish discourse connectives built by automatic means. The lexicon has the format of the German connective lexicon, DiMLex, where for each discourse connective, information about the connective‘s orthographic variants, syntactic category and senses are provided along with sample relations. In this paper, we describe the data sources we used and the development steps of the lexicon.


Introduction
Discourse connectives (alternatively labelled as cue phrases, discourse markers, discourse operators, etc.) are lexical anchors of coherence relations. Such relations (with semantic labels such as expansion, contingency, contrast, concession) can be signalled with discourse connectives, but languages vary in the way they express them. For example, while languages like English and German express discourse relations lexically (with conjunctions and adverbials), Turkish conveys discourse relations through morphological suffixes, as well as lexically. Languages also diverge in the number of connectives that express the same discourse relation. For example, French and Dutch differ in the number of connectives that convey causal relations (Zufferey and Degand, 2017). Finally, discourse connectives are polysemous, expressing several discourse relations. These issues are an obvious challenge for language technologies, translation studies and language learners. What is needed is a resource that goes beyond traditional dictionaries. Our goal in this paper is to reveal the nature of Turkish discourse connectives through discourse-annotated corpora and describe the steps in constructing a discourse connective lexicon that hosts the connectives' various properties. The Turkish Lexicon will ultimately be part of the connective lexicon database (http:// connective-lex.info/) that aims to synchronize the lexicons that exist.
The interest in discourse connectives goes hand in hand with the development of discourseannotated corpora. There are three major approaches that have guided discourse research and inspired other languages to annotate discourse: RST (Mann and Thompson, 1988), SDRT (2012), and the PDTB (Prasad et al., 2014). Our focus in this paper will be the PDTB, one of the best known resources for English discourse. The PDTB takes discourse connectives (henceforth, DCs) as twoplace predicates where argumenthood is based on abstract objects (eventualities, facts, propositions, etc.) as in Asher (1993). It annotates the DC together with its binary arguments, which are semantic representations of discourse parts (cf. (Danlos, 2009)). The PDTB-style annotation has been extended to various languages other than English, namely, Arabic (Al-Saif and Markert, 2010), Chinese (Zhou and Xue, 2015), Hindi (Kolachina et al., 2012), and Turkish (Demirşahin and Zeyrek, 2017) as well as a recent multilingual resource, TED-Multilingual Discourse Bank, or TED-MDB (Zeyrek et al., 2019).
In addition to these efforts, there has been an important initiative, namely DiMLex, the discourse connective lexicon first developed for German (Stede and Umbach, 1998;Scheffler and Stede, 2016), which has subsequently been extended to multiple languages, e.g. French (Roze et al., 2012), Italian (Feltracco et al., 2016), Portuguese (Mendes et al., 2018) and recently Eng-DiMLex for English (Das et al., 2018). Such lex-icons are sure to complement the ongoing efforts of discourse-annotated corpora, support discourse research and various language technology applications such as discourse parsers.
In this paper, we describe the development of TCL, a lexicon for Turkish discourse connectives, which follows the format of DiMLex. To the best of our knowledge, there is no such resource for Turkish. Thus, our aim is to fill this gap with a resource that covers Turkish discourse connectives with their various properties and a representation of their meanings. This resource will not only benefit discourse studies in Turkish but will also form the basis of future multilingual studies on discourse connectives and their meanings.
In the rest of this paper, we describe the steps in creating the TCL. In Section 2, we provide information about the data sources we used and in Section 3, we discuss the criteria for selecting connectives as TCL entries. Section 4 presents the structure of TCL and Section 5 shows how the lexicon is populated. Section 6 brings the paper to an end and draws some conclusions.

Data Sources
In building the TCL, we use three PDTBinspired annotated corpora to compile explicit DCs, namely, Turkish Discourse Bank or TDB 1.0 (Zeyrek et al., 2013), TDB 1.1 (Zeyrek and Kurfalı, 2017), and the Turkish section of TED-MDB.
• TDB 1.0 is a 400,000-word resource of modern written Turkish containing annotations of explicit DCs and the discourse segments they relate. It also annotates "phrasal expressions" such as bunun için 'for this (reason/purpose)', which are linking devices compositionally derived from postpositions (için 'since/in order to') and a deictic term. They are a subset of the PDTB's alternative lexicalizations and correspond to "secondary connectives" (Danlos et al., 2018). We used 8439 relations (explicit DCs and "phrasal expressions") from this corpus.
• TDB 1.1 is a 40,000-word-subset of TDB 1.0, where all five relation types of the PDTB are annotated together with their binary arguments (i.e., explicit and implicit relations, alternative lexicalizations, entity relations and no relations). Based on the PDTB-3 relation hierarchy (Lee et al., 2016), the senses of explicit and implicit connectives as well as alternative lexicalizations are annotated. We used 912 explicit relations from this corpus.
• TED-MDB is a corpus of TED talks transcripts in 6 languages (English, German, Polish, European Portuguese, Russian and Turkish). We used 276 explicit relations from the Turkish section of this corpus. TDB 1.0 and TED-MDB annotation files are in pipedelimited format, the TDB 1.1 annotation files are in XML format.
By using different resources, we take advantage of the different coverage of the three corpora. As expected, while some of the connectives exist in all of the data sources, some connectives (and the information needed for the connective lexicon database) may exist in only one source (see Table 2). Moreover, resorting to different corpora is helpful as different corpora may spot new senses of a DC. For example, different senses of the postpositions gibi 'as' and kadar 'until/as well as/as much as' have been compiled from different corpora as indicated in Table 1.

The criteria for selection of connectives as TCL entries
Turkish is a morphologically rich, agglutinating language with suffixes added to the word root in the order licensed by the morphology and syntax of the language. In this section we describe the major syntactic categories we used to determine DCs, and how we represent suffixal connectives (converbs) in TCL. We also explain our method of determining the syntactic category of other DCs when different POS taggers provide different parses. TCL only considers explicit discourse connectives annotated in the existing Turkish discourseannotated corpora. Unlike other DC lexicons such as DimLex and the lexicon of Czech discourse connectives (Mírovskỳ et al., 2017) it does not record non-connective usages.

Major syntactic categories
DCs are determined on the basis of the following syntactic categories: • Conjunctions, comprising both the single type ama 'but/yet' and the paired or noncontinuous type such as ne . . . ne 'neither . . . nor'.   which involve an accompanying suffix on the (non-finite) verb of the subordinate clause, gibi 'as'.
• Adverbs, involving single tokens such as ayrıca 'in addition' as well as phrasal tokens, e.g. ne var ki 'even so'. 1
Converbs have complex allomorphy based on vowel harmony as well as consonant harmony (Zeyrek and Webber, 2008). We decided that such variation has to be represented in TCL. To illustrate, -(y)ArAk 'by means of/and' is a converb shown in the standard morphological notation, where the capital letters indicate alternation (-erek, -arak) and the parentheses show that y is needed if the verb root ends in a vowel (see examples (1), (2), (3)). Other converbs may additionally carry dedicated nominalization markers or person agreement markers, which have different morphological realizations. To identify all occurrences of a converb, the allomorphs need to be specified in 1 We note that the TDB's term "phrasal expression" is different from the DimLex term "phrasal connective", which refers to discourse connectives that involve more than one words without specifying the type of words involved in the composition of the connective. the lexicon. By means of the TCL search tool (see 5.1 below), we specified 15 converbs and their allomorphs to be added to TCL. If any allomorph of a specific converb was missing in the corpora we used, those allomorphs were added manually. (1) Ali okula gid-erekögretmenle görüştü. 2 'Ali went to school and talked with the teacher.' (2) Ali sıkı çalış-arak başarı kazandı. 'Ali gained success by working hard.' (3) Ali şarkı söyle-yerek başarı kazandı. 'Ali gained success by singing.'

Noun-based connectives
Turkish has a group of connectives which are the lexicalized forms of nominal roots, e.g. dahası 'furthermore', amacıyla 'with the aim of', sonuçta 'eventually.' For this group of connectives, the available POS taggers sometimes provide incomplete information.  In such cases, we compare different sources to determine the connective's syntactic category for TCL. For example, for the connectives in Table 3, we settled on the syntactic categories provided in Table 4.  The TCL structure is based on the structure of the connective lexicon database. Thus, it contains the following components.
• Orthographical variants: This criterion specifies whether the connective is a single token (part=single) or a phrasal token (part=phrasal); continuous (orth=cont) or discontinuous (orth=discount). For example, the phrasal connective ne...ne 'neither . . . nor' is annotated as "discont" while the connectiveöte yandan 'on the other hand' is annotated as "cont". An entry illustrating the orthogrophical variants of the single connective ama 'but' is provided in Figure 1. In addition to these, we added the type "suffixal" to TCL to indicate converbs.
• Canonical Form: The canonical form of a connective is the most commonly used variant of that connective. For example, the canonical form of çünkü 'because' is the sentence-initial Çünkü, a property which is determined by the TCL search tool.
• Frequency: The frequency of the connective shows both how often it occurs in the corpora and the frequency of each of its sense tags.
• Syntactic category: The syntactic category of connectives is assigned using several sources as described in Section 5.1, namely the Turkish section of UDPipe 3 , the search tool pro-3 http://lindat.mff.cuni.cz/services/udpipe/

The TCL search tool
We developed a search tool to populate TCL. The search tool was developed using the C# programming language. It is also extendable with new features. The main feature of the tool is that it searches different corpora to retrieve DCs (see Figure 2 for a snapshot of the main window of the tool). In addition, it uses filters for DC types, such as suffixal, single, and phrasal. When the search tool is started, file paths used by the tool are specified in the data path window, namely, the path of the text directory specifying the raw text files that will be searched, and the path of the annotation directory containing the XML or pipe-delimited files storing the annotation information.

The workflow
Using the search tool, we populated the TCL entries. Our work flow involves several steps, as described below and summarized in Figure 3.
• Firstly, the annotation files of the three corpora are parsed and the relations encoded by explicit connectives are retrieved. For this purpose, an XML parser and a pipe-delimited file parser have been developed.
• Relation Builder: The Relation Builder module reads the connective and its sense(s) in each relation directly from the annotation files while it reads the respective relation • DC Lexicon Entry Builder: Using the two lists of relations from the Relation Builder, this module computes the following properties for each DC to be represented in the lexicon: -Orthographical Variants: Since suffixal connectives are uniform in terms of orthography, continuity, etc. they do not have variants. Therefore, the DC Lexicon Entry Builder only searches the list of non-suffixal relations to determine the variants of all connectives. Suffixal connectives are simply tagged as "suffixal". -Allomorphs: To handle the allomorphs of converbs, the list of suffixal relations is used. The entries of this list are analyzed to find out which entries are allomorphs of a suffixal DC. -Continuity: Phrasal DCs can be continuous or discontinuous. This property is specified automatically by going through the multi-word connectives in the list of non-suffixal relations, and whether there are any words between the two parts of the connective is determined. • Syntactic Category Tagger: This module assigns a syntactic category to each DC. If a connective is suffixal, it is assigned the converb category. The syntactic category of nonsuffixal connectives is determined on the basis of the available POS taggers to the extent possible, otherwise by comparing various parses as described in Section 4 above.
Some of the connectives may belong to more than one syntactic category (Zeyrek and Kurfalı, 2018). For such connectives, we provide both of the syntactic categories. E.g. the DC once 'before' is both an adverb (4) and a postposition (5).
'Before seeing this movie, you should read the novel.' The syntactic categories we assign to the DCs are; CCONJ, Converb (Simplex Subordinator), ADP (Postposition, Complex Subordinator), ADV, Secondary Connective ("phrasal expressions") and Other categories, such as noun-based connectives. These syntactic categories are mapped onto the categories which the Connective-Lex website offers (cf. Table 5).
• DiMLex Formatter: After creating the list of DC lexicon entries with all the properties described so far, the entries are mapped onto the DiMLex XML format. Firstly, an XML document is created and for each entry of the lexicon, an XML node is created. The XML elements and attributes are filled with the properties of lexicon entries following the Dim-Lex format.
• DCs in the DiMLex format: At the end of these steps, we have a list of 180 DCs with their respective syntactic categories and other properties.
In Figures 4, 5 and 6, we provide how DCs are represented in the DiMLex format. Figure 4 presents the entry of a suffixal connective, Figure 5 shows the entry for a single connective belonging to the postposition category and Figure 6 illustrates the entry for a phrasal discontinuous connective.
We computed the sense distribution of Turkish explicit DCs by using our corpora and compared the results with the sense distribution of explicits in the PDTB 2.0 (Prasad et al., 2014). Table 6 Table 7 provides the most frequent 15 discourse connectives and their second-level senses compiled from all data sources.

Conclusion
In sum, the major contributions of this paper have been: • to characterize various properties of Turkish discourse connectives including their syntactic categories and the senses they convey via discourse-annotated corpora, • to develop a DimLex-style lexicon of discourse connectives to host Turkish discourse connectives together with their various properties and sample relations retrieved from annotated corpora.
TCL is populated by DCs gleaned from texts belonging to different genres. Given that DCs are sensitive to genre (Webber, 2009), in future work, we will compute the distribution of senses in different genres and work on incorporating this information into DiMLex. This aim goes in parallel with our plan of extending the DC search tool with new facilities.