English. Nominal utterances are very frequent, especially in social media texts, and play a crucial role as they are very dense from a semantic point of view. In spite of this, their automatic identification has received little to no attention. We have thus developed a framework for the annotation of nominal utterances and created the manually annotated corpus COSMIANU (Corpus Of Social Media Italian Annotated with Nominal Utterances), which could be used to train automatic systems.
Syntactic declarative constructions built around a
non-verbal head (as in, for example, “What a nice
movie!”) are very common linguistic phenomena
in many Indo-European, Slavic and Semitic
languages (such as Latin, Hebrew, Arabic, Russian,
English, Spanish, and Italian), as well as in
FinnoUgric and Bantu languages
It has been clearly shown that nominal
utterances (NUs) occur with relatively high frequency
not only in spoken language
Yet NUs pose significant challenges when it comes to both their automatic processing, because of the absence of a verbal head, and identification, due to the fact that they can have diverse syntactic structures, containing, for example, dependent clauses with finite verbs.
So far, little or no attention has been paid to the identification and processing of NUs in NLP areas such as information extraction/retrieval, sentiment analysis, and opinion mining. However, in order to address newly emerging challenges, these research fields could greatly benefit from tackling NUs specifically. This is the case, for instance, with aspect-based sentiment analysis, which aims to identify the main (e.g., the most frequently discussed) aspects (e.g., food, service) of given target entities (e.g., restaurants) and the sentiment expressed towards each aspect, instead of detecting the overall polarity of a text span (as sentiment analysis usually does). Similarly, argumentation mining, which takes one step forward with respect to opinion mining by extracting not only information about people’s attitudes and opinions, but also about the arguments they give in favor of and against their target entities (e.g., products, institutions, politicians, celebrities, etc.), could dramatically improve by focusing on NUs, which are often used, just like slogans, as the most emphatic part of the argumentation.
As a first step towards enabling automatic systems to process NUs, we have developed a complete framework for their annotation, and have created the Corpus Of Social Media Italian Annotated with Nominal Utterances (COSMIANU), which will be freely distributed with a Creative Commons (CC-BY) licence and can therefore be used to train automatic systems.
In this paper, we first summarize the main criteria adopted for the annotation of NUs (Section 3); in Section 4 we describe the annotated corpus; in Section 5 we present the results of some preliminary experiments on automatic identification of NUs, and finally, in Section 6, we draw some conclusions. 2
The first corpus-based study of NUs was part of
the C-ORAL-ROM project, a multilingual
(Italian, French, Portuguese and Spanish) corpus
composed by 1,200,000 words of spontaneous speech,
created in order to describe the prosodic and
syntactic structures of romance languages
Relatively similar is the study conducted on the
AN.ANA.S Multilingual Treebank, consisting of
21,300 words of spontaneous speech and
taskoriented dialogues in Italian, English and Spanish,
manually annotated in order to identify verbless
clauses
In more recent work, Garcia-Marchena (2016) uses the Spanish open-source corpus CORLEC1 to manually identify and classify over 7,000 verbless utterances in a detailed taxonomy.
While the above-mentioned studies all address verbless sentences and clauses, the phenomenon in which we are interested is wider and includes more complex syntactic structures, partly because we address nominal utterances, which is a wider
Lengua Espan˜ola Contempora´nea, http://www.lllf.uam.es/ING/Corlec.html set than verbless utterances (in our perspective, in fact, the main clause of a NU can govern dependent clauses with finite verbs). For this reason we devised a complete annotation framework. Moreover, to the best of our knowledge, our work is the first attempt towards a corpus-based study of NUs on written texts (Cresti (2004), Landolfi et al. (2010), and Garcia-Marchena (2016) address spoken language). 3
In the following, we provide a brief summary of
the annotation framework we devised for the
manual annotation of NUs, which is based on the
literature on NUs in Italian
According to the annotation schema we propose, every utterance whose main clause is non-verbal, i.e. it does not contain a finite verb (see (1)), is marked as a Nominal Utterance (NU); note, however, that a non-verbal main clause can contain non-finite verbs, such as infinitive and/or participial forms and gerunds (see (2), (3), and (4)). (1) <NU>Felicissima per il suo ritorno!</NU> [Very happy about his return!] (2) <NU>Ma impegnarsi di piu`?</NU> [Why not put more effort into it?] (3) <NU>Spariti i negozi, l’edicola, posteggio.</NU> [Shops, news stand, and car park, all gone.] il (4) <NU>Facendo due conti.</NU>
[Doing the math.] 3.2
When the main clause of an utterance bears a coordination relation to another clause, the NU is annotated as follows:
If both are non-verbal, the extent of the NU includes them both (see (5)); 2This document is available for consultation from http://tiny.cc/auhvvy If one is verbal and the other one is nonverbal, the extent of the NU includes only the non-verbal one (see (6)). (5) <NU>Acqua a dirotto e tutti a casa!</NU> [Too much rain and everyone home!] (6) <NU>I lavori prima,</NU> e poi si cena.
[Chores first, and then we’ll eat dinner.]
Due to their peculiar syntactic structure, NUs with coordination are further marked with the attribute “verbal-coordinate” (coordination of verbal and non-verbal clauses) or “non-verbalcoordinate” (coordination of non-verbal clauses). 3.3
Non-verbal subordinate clauses are included in the extent of an NU, as in (7), whereas verbal subordinate clauses are not, as in (8) and (9). (7) <NU>Che bello partire tutti quanti!</NU> [Great to leave all together!] (8) <NU>Felice</NU> che ti sia piaciuta.
[Glad you liked it.] (9) Siccome piove, <NU>tutti a casa.</NU> [As it is raining, everyone home.]
NUs with verbal subordinate clauses are marked with a specific attribute, i.e., “verbal-subordinate”. 3.4
As explained above, NUs are utterances whose
main clause is non-verbal, i.e. it does not contain
a finite verb. Unlike in other NUs, in ellipses it
is always possible to infer the omitted verb
Ellipses are marked, using the specific attribute “ellipsis”, both when the preceeding utterance is written by a different user, as in (10) and when it is written by the same user, as in (11). (10) Cosa vorresti per cena? [What would you like for dinner?] <NU>Una pizza!</NU> [A pizza!] (11) Cosa voglio??? [What do I want???] <NU>Del rispetto!</NU> [Some respect!]
Forums
Newsgroups
Soc. networks
Total
COSMIANU contains texts taken from the
Web2Corpus IT
These texts consist of discussions between users across a large number of themes (from politics to popular singers). Thus in most cases, users interact with each other creating a dialogic enviroment rich in verbal crossfires and quotes. This kind of interactions are a particularly fertile ground for ellipses and NUs in the form of greetings, which are usually very frequent in spoken language.
Automatic pre-proccessing of the corpus, for
which we used the TextPro suite of NLP tools
The manual annotation was then performed by
an expert annotator using the Content Annotation
Tool (CAT)
In order to evaluate the inter-annotator agreement, a subpart of the corpus consisting of 5,193 tokens was annotated by a second annotator. The resulting Dice coefficient is 87.40. Both annotators identified 127 NUs, 111 of which are common (evaluation based on exact match).
Table 2 reports, for both the whole corpus and for each subcategory, the total number of NUs and the number of NUs marked with each specific attribute, i.e. “verbal-coordinate”, “non-verbalcoordinate”, “verbal-subordinate”, and “ellipsis” (NUs that are not marked with any attribute, such as (1), (2), (3), and (4), are referred to as “simple NUs”).3
In the whole corpus we annotated 1,024 NUs, which means that 20,6% of the sentences contain an NU. This percentage is lower than those reported by Cresti (2004) (38,1%) and Landolfi et al. (2010) (28%). This can be explained by the fact that the above-mentioned studies focus on spoken language, where interrupted strings, brachyologies and turn-taking cues are more frequent with respect to written language. Still, this percentage shows that the nominal style is well represented in written informal Italian, most likely due to its linguistic economy and to its high semantic density, which are particularly useful for expressing emphasis (see (12)). (12) <NU>Dichiarazione da Mr. Hyde!</NU> [A statement worthy of Mr. Hyde!]
In addition, the large number of NUs marked as coordinate, either “verbal” (140 NUs) or “nonverbal” (58 NUs) shows that parataxis is constant throughout these texts. In fact, NUs appear to be extremely suitable to the parataxis typical of CMC; furthermore, they are often isolated, i.e., free from hierarchical syntactic bonds. This also explains why NUs can be composed of a series of 3Notice that a single NU can be marked with more than one attribute. denotative elements simply listed without any explicit hierarchical bond, as in (13), in a way that reminds one of a list of keywords. (13) <NU>Buon senso, etica, vincere tanto per vincere.</NU> [Common sense, ethics, winning for winning’s sake.]
Looking at the distribution of NUs in the four
subcategories, we see that social networks have
the highest number of NUs (304), despite
having a significantly lower number of tokens than
blogs, forums and newsgroups. This probably
depends on the high perceived communicative
economy typical of social networks
In Table 3 we report the co-occurence of NU attributes by pairs4 in order to show how diverse syntactic structures NUs can have. Particularly interesting is the presence of 38 NUs containing ellipses coordinated with a verbal clause; in fact, the ellipsis usually follows the verbal clause, whose verb is implied in a contrastive context. Additionally, ellipses can support a verbal subordinate clause (in our corpus we have 26 cases), which usually adds further information in favor of the contrastive utterance (see (14)).
4Although we have case where NUs have been marked with up to four attributes, we only focus on co-occurrence by attribute pairs. (14) Non e` un edificio specifico, <NU> ma una tipologia architettonica </NU> che caratterizza l’URSS. [It is not a specific building, but an architectural typology that characterizes the USSR.] 5
We used COSMIANU to train an open source SVM classifier, YamCha5, and performed some preliminary experiments on NU identification. As training data, we selected 44,170 tokens (i.e. about 2/3 of the corpus) while maintaining the same proportion of blogs, forums, newsgroups, and social networks over the whole corpus. We used the remaining part of the corpus (21,841 tokens) as a test set. In these preliminary experiments we also included the NUs that appear in the text as metadata, which are annotated and marked with the specific tag “metadata” in COSMIANU, as shown in Example (15) 6. The training set and the test set thus contain respectively 1,775 and 1,058 NUs. (15) <NU> Data: 27/09/2010. </NU> [Date: 09/27/2010.]
We pre-processed the data using the TextPro
suite
Baseline W2+Tok+Lem+Pos
Prec. 33.80 79.80
Rec. 27.13 67.96
5Yet Another Multipurpose CHunk Annotator. Website: http://chasen.org/ taku/software/yamcha/
6Metadata usually refer to when and where a certain message has been written; although “metadata” NUs are very frequent in the corpus (more than 60% of the total), they are not particularly interesting from a linguistic point of view and we did not include them in the counts of Section 4.
This work shows how common NUs are in written informal language, as well as how important they are in conveying semantically dense concepts in emphatic informative peaks, which could be useful for many NLP fields (e.g., argumentation mining and aspect-based sentiment analysis).
By creating COSMIANU, an Italian corpus annotated with NUs, and making it freely available to the research community, we made a first step towards the development of automatic tools for the identification and classification of NUs. In our preliminary experiments on NU identification (performed using an SWM classifier), with our best configuration, we obtained a performance of 73.40% in terms of F1 on all NUs (i.e. including metadata).
In the future, we intend to further expand COSMIANU, both in terms of its size and in terms of the annotations it includes, hoping that this will encourage more research on this extremely common, and yet almost neglected, linguistic phenomenon. We also plan to work on the analysis and automatic recognition of NUs, especially when they are used to convey hate speech, in the form of racist, sexist, homo/transphobic or classist slogans and insults.
We would like to thank Isabella Chiari for providing us the Web2Corpus IT, from which we selected the raw texts to build COSMIANU. We also thank our colleagues Roberto Zanoli and Rachele Sprugnoli for their valuable advice and contributions in performing the experiments and defining the annotation guidelines.
Francesco Sabatini and Vittorio Coletti.
Dizionario Italiano Sabatini-Coletti.
Firenze, Italia.
1997.
Giunti,