C oll F r E n: Rich Bilingual E nglish– F rench Collocation Resource (2020)

Abstract: Collocations in the sense of idiosyncratic lexical co-occurrences of two syntactically bound words traditionally pose a challenge to language learners and many Natural Language Processing (NLP) applications alike. Reliable ground truth (i.e., ideally manually compiled) resources are thus of high value. We present a manually compiled bilingual English–French collocation resource with 7,480 collocations in English and 6,733 in French. Each collocation is enriched with information that facilitates its downstream exploitation in NLP tasks such as machine translation, word sense disambiguation, natural language generation, relation classification, and so forth. Our proposed enrichment covers: the semantic category of the collocation (its lexical function), its vector space representation (for each individual word as well as their joint collocation embedding), a subcategorization pattern of both its elements, as well as their corresponding BabelNet id, and finally, indices of their occurrences in large scale reference corpora.

## introduction collocations in the sense of idiosyncratic lexical co-occurrences of two syntactically bound words are central to second language (l2) learning @xcite @xcite @xcite and various nlp applications -including, e.g., word sense disambiguation @xcite , parsing and machine translation @xcite , and natural language generation @xcite . however, manually compiled and semantically annotated large scale collocation datasets are scarce. 1 even more scarce are aligned multilingual collocation resources, which are instrumental for any crosslanguage application. in what follows, we present a manually compiled and semantically annotated bilingual (english-french) collocation resource. in order to facilitate its uptake in different applications, we enrich the collocations in this resource with additional information: each collocation is assigned its semantic category in terms of a lexical function @xcite and its corresponding relation embedding @xcite . the individual collocation elements are also embedded using @xcite 's skipgram algorithm. the functional head of each collocation is furthermore disambiguated against babelnet @xcite , which facilitates the alignment between the equivalent english and french heads (as, e.g., between eng. charges and fr. accusations in dismiss the charges and rejeter les accusations). to allow for the consultation of the use of a collocation in context (be it for second language learning or model training), for each collocation, sentences from large scale english and french corpora in which they occur are also released. the remainder of the paper is structured as follows. in section 2, we provide some background on the notion of collocation, point to some of the available collocation (or, in more general terms, multiword expression) resources, and introduce the concept of lexical function. section 3 outlines the types of information by which we enrich each collocation in our english and french collocation lists. section 4 describes how the resource is organized. section 5 finally, draws some conclusions from the presented resource and outlines several tasks that we are about to tackle in order to enhance and further enrich it. ## background despite the fact that an increasing number of works addresses the challenge of collocation extraction and classification, the diverging interpretations of the term "collocation" that underline these works call for a clear statement of what we mean by "collocation", before providing any further details on our resource. in what follows, we thus fist define the notion of "collocation" as we use it. in the following subsections we then review the available resources of multiword expressions, of which collocations are one type, and introduce the lexical function-based categorization in terms of which the collocations in our resource are classified. ## corpus preparation the development of this resource consisted of a two-step procedure: first, corpus processing and indexing; and second, collocation matching and assembling the resource in the desired output format. in the first step, we apply a syntactic parser to obtain the part-of-speech (pos) tags and dependency relation information for the sentences in the corpus. once processed, the sentences are indexed in a solr search engine. in the second step, each collocation is searched for in the index. pos tags and dependencies are used to retrieve the sentences in which the base and the collocate co-occur distribution-wise and are related syntactically. for this purpose, first, a query is applied to search for a specific syntactic relation between the base and the collocate (e.g., verb-object or head-modifier). this query guarantees a high precision, but could result in a low recall for some collocations due to, e.g., parsing errors or difficulty to l.id b bn.id bpos c lf st. end q s sentence fragment en wrong bn:00104880a a terribly magn 5 6 4 1 he knew something was terribly wrong . en wreath bn:00017726n n lay causfunc2 4 5 4 1 prince charles to lay wreath at graves en wrath bn:00081680n n incur oper2 5 7 4 1 the foundation has also incurred the wrath of many in the exile ... en cold feet bn:00020546n n get incepoper1 9 11 1 1 but in the white house , some are getting cold feet en turn out bn:00085376v v well bon 3 5 1 1 when things turned out well , they walked away with huge bonuses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ## conclusions we presented a bilingual english-french collocation resource in which the collocations are tagged with respect to lexical functions and enriched by information that is commonly used by state-of-the-art nlp applications. this information concerns, in particular, subcategorization patterns, embeddings of the collocation elements and embeddings of the collocation relations and indices of the collocation occurrences in the reference corpora. for disambiguation and interlinking of the bases in the english and french collocation lists, we use babelnet senses. the presented resource can be used either as an input to nlp applications or as an online collocation dictionary. in this latter interpretation it resembles the online collocation dictionary dice of spanish http://www.dicesp.com/ . as our resource, dice classifies collocations in terms of lexical functions, provides the subcategorization information of both the base and the collocate and cites examples of the use of each collocation extracted from a large scale corpus. however, while dice includes only some selected examples of the occurrence of each collocation, in our resource, each collocation is indexed with all of its occurrences in the corpus. furthermore, dice does not contain any embeddingrelated information on the collocation elements or the collocational relations. the resource is available at https://github.com/talnupf/collfren . as part of future work, we plan to align the french and english collocation equivalents (not only the bases), completing the lists when no equivalent is available in the present list. furthermore, we plan to automatically extend the resource using state-of-the-art collocation extraction and semantic classification techniques, also for other languages than english and french. in this context, the resources created in the parseme cost action https://typo.uni-konstanz.de/parseme/ will be also explored.