Lexical Semantic Recognition (2021)

Abstract: In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.

## introduction many nlp tasks traditionally approached as tagging focus on lexical semantic behavior-they aim to identify and categorize lexical semantic units in running text using a general set of labels. two examples are supersense tagging of nouns and verbs as formulated by @xcite , and verbal multiword expression (mwe) identification and classification in the multilingual parseme shared tasks @xcite @xcite . by analogy with named entity recognition, we can use the term lexical semantic recognition (lsr) for such chunking-and-labeling tasks that apply to lexical meaning generally, not just entities. this disambiguation can serve as a foundational layer of analysis for downstream applications in natural language processing, and provides an initial level of organization for compiling lexical resources, such as semantic nets and thesauri. in this paper, we tackle a more inclusive lsr task of lexical semantic segmentation and disambiguation. the streusle corpus (see §2) contains comprehensive annotations of mwes (along with their holistic syntactic status) and noun, verb, and preposition/possessive supersenses. we train a neural crf tagger @xcite using bert embeddings @xcite and find that it obtains strong results as a first baseline for this task in its full form. in addition, we ask: does a tagger trained on streusle generalize to evaluations like the parseme shared task on verbal mwes @xcite and the dimsum shared task on mwes and noun/verb supersenses @xcite ? results show our lsr model based on streusle is general enough to capture different types of analysis consistently, and suggest an integrated full-sentence tagging framework is valuable for explicit modeling of lexical semantics in nlp. foot_0 ## lsr tagging frameworks our tagger is based on streusle (supersense-tagged repository of english with a unified semantics for lexical expressions; @xcite ), foot_1 a corpus of web reviews annotated comprehensively for lexical semantic units and supersense labels. specifically, there are three annotation layers: multiword expressions, lexical categories, and supersenses. the supersenses apply to noun, verb, and prepositional/possessive units. figure 1 shows an example. many of the component annotations have been applied to other languages: verbal multiword expressions @xcite , noun and verb supersenses (e.g., picca et al., ## modeling we develop and evaluate a strong neural sequence tagger on the full task of lexical semantic recognition with mwes and noun/verb/preposition/possessive supersenses to assess the performance of modern techniques on the full joint tagging task. our tagger feeds pre-trained bert representations @xcite through a bilstm. an affine transformation followed by a linear chain conditional random field produces the final output. for further implementation details, see appendix b. the predicted tag for each token is the conjunction of its mwe, lexcat, and supersense. 7 there are 572 such tags in the streusle training set, and only 12 unique conjoined tags in the development set are unseen during training (≈5% of the development set tagging space, corresponding to ≈0.2% of the tokens in the development set). constrained decoding. a few hard constraints are imposed in tagging. to enforce valid mwe chunks, we use first-order viterbi decoding with the appropriate corpus-specific constraints (e.g., for streusle mwes, the bbiioo _ ~tagset; see §2.1.1). the mwe constraint is applied during training and evaluation. in addition, a given token's possible lexcats are constrained by the token's pos tag and lemma. for instance, a token with the aux upos tag can only take the aux lexcat. however, if the token's upos is aux and its lemma is "be", it can take either the aux or v lexcats. the pos and lemma constraints are only applied during evaluation; to avoid relying on gold pos/lemma annotations at test time we use an offthe-shelf system @xcite . ## related work the computational study of mwes has a long history @xcite @xcite @xcite @xcite , as does supersense tagging @xcite . @xcite developed a sequence tagger for both mwes and named entities in english. @xcite ; @xcite featured joint tagging of mwes and noun and verb supersenses with featurebased sequence models. richardson ( 2017 ) trained such a model on streusle 3.0 as a noun, verb, and preposition supersense tagger (without modeling mwes). for preposition supersenses, gonen and goldberg (2016) incorporated multilingual cues; @xcite experimented with feature-based and neural classifiers; and @xcite , modeling supersense disambiguation of single-word prepositions only, found pretretrained contextual embeddings to be much more effective even with simple linear probing models. ## conclusion we study the lexical semantic recognition task defined by the streusle corpus, which involves joint mwe identification and coarse-grained (supersense) disambiguation of noun, verb, and preposition expressions; this task subsumes and unifies the previous parseme and dimsum evaluations. we develop a strong baseline neural sequence model, and see encouraging results on the task. furthermore, zero-shot out-of-domain evaluation of our baselines on partial versions of the task yields scores comparable to the fully-supervised in-domain state of the art.