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Automatic Animacy Classification Automatic Animacy Classification Samuel R. Bowman Harshit Chopra Department of Linguistics Department of Computer Science Stanford University Stanford University 450 Serra Mall 353 Serra Mall Stanford, CA 94305-2150 Stanford, CA 94305-9025 [email protected] [email protected] Abstract machine translation. We introduce the automatic annotation of The handful of papers that we have found on noun phrases in parsed sentences with tags animacy annotation—centrally Ji and Lin (2009), from a fine-grained semantic animacy hierar- Øvrelid (2005), and Orasan and Evans (2001)— chy. This information is of interest within lex- classify only the basic ANIMATE/INANIMATE con- ical semantics and has potential value as a fea- trast, but show some promise in doing so. Their ture in several NLP tasks. work shows success in automatically classifying in- We train a discriminative classifier on an an- dividual words, and related work has shown that an- notated corpus of spoken English, with fea- imacy can be used to improve parsing performance tures capturing each noun phrase’s constituent (Øvrelid and Nivre, 2007). words, its internal structure, and its syntactic relations with other key words in the sentence. We adopt the class set presented in Zaenen et al. Only the first two of these three feature sets (2004), and build our model around the annotated have a substantial impact on performance, but corpus presented in that work. Their hierarchy con- the resulting model is able to fairly accurately tains ten classes, meant to cover a range of cate- classify new data from that corpus, and shows gories known to influence animacy-related phenom- promise for binary animacy classification and ena cross-linguistically. They are HUMAN, ORG (or- for use on automatically parsed text. ganizations), ANIMAL, MAC (automata), VEH (vehi- cles), PLACE, TIME, CONCRETE (other physical ob- 1 Introduction jects), NONCONC (abstract entities), and MIX (NPs An animacy hierarchy, in the sense of Zaenen et al. describing heterogeneous groups of entities). The (2004), is a set of mutually exclusive categories de- class definitions are straightforward—every NP de- scribing noun phrases (NPs) in natural language sen- scribing a vehicle is a VEH—and Zaenen et al. of- tences. These classes capture the degree to which fer a detailed treatment of ambiguous cases. Unlike the entity described by an NP is capable of human- the class sets used in named entity recognition work, like volition: a key lexical semantic property which these classes are crucially meant to cover all NPs. has been shown to trigger a number of morphologi- This includes freestanding nouns like people, as well cal and syntactic phenomena across languages. An- as pronominals like that one, for which the choice of notating a corpus with this information can facili- class often depends on contextual information not tate statistical semantic work, as well as providing a contained within the NP, or even the sentence. potentially valuable feature—discussed in Zaenen et In the typical case where the head of an NP be- al.—for tasks like relation extraction, parsing1, and longs unambiguously to a single animacy class, the phrase as a whole nearly always takes on the class 1Using our model in parsing would require bootstrapping from c oarser parses, as our model makes use of some syntactic of its head: The Panama hat I gave to my uncle features. on Tuesday contains numerous nominals of differ- ent animacy classes, but hat is the unique syntactic and its POS tag. HEADSHAPE-tag-shape attempts head, and determines the phrase to be CONCRETE. to cover unseen head words by replacing the word Heads can easily be ambiguous, though: My stereo string with its orthographic shape (substituting, for speakers and the speakers at the panel session be- example, Stanford with Ll and 3G-related with dL- long to different classes, but share a (polysemous) l). head. The corpus that we use is Zaenen et al.’s animacy- 2.3 External syntactic features annotated subset of the hand-parsed Switchboard The information captured by our tag set overlaps corpus of conversational American English. It is considerably with the information that verbs use to built on, and now included in, Calhoun et al.’s select their arguments.3 The subject of see, for ex- (2010) NXT version of Switchboard. This anno- ample, must be a HUMAN, MAC, ANIMAL, or ORG, tated section consists of about 110,000 sentences and the complement of above cannot be a TIME. As with about 300,000 NPs. We divide these sentences such, we expect the verb or preposition that an NP into a training set (80%), a development set (10%), depends upon and the type of dependency involved and a test set (10%).2 Every NP in this section is ei- (subject, direct object, or prepositional complement) ther assigned a class or marked as problematic, and to be powerful predictors of animacy, and introduce we train and test on all the NPs for which the an- the following features: SUBJ(-OF-verb), DOBJ(-OF- notators were able to agree (after discussion) on an verb) and PCOMP(-OF-prep)(-WITH-verb). We ex- assignment. tract these dependency relations from our parses, and mark an occurrence of each feature both with 2 Methods and without each of its optional (parenthetical) pa- We use a standard maximum entropy classifier rameters. (Berger et al., 1996) to classify constituents: For 3 Results each labeled NP in the corpus, the model selects the locally most probable class. Our features are de- The following table shows our model’s precision scribed in this section. and recall (as percentages) for each class and the We considered features that required dependen- model’s overall accuracy (the percent of labeled cies between consecutively assigned classes, allow- NPs which were labeled correctly), as well as the ing large NPs to depend on smaller NPs contained number of instances of each class in the test set. within them, as in conjoined structures. These achieved somewhat better coverage of the rare MIX Class Count Precision Recall class, but did not yield any gains in overall perfor- VEH 534 88.56 39.14 mance, and are not included in our results. TIME 1,101 88.24 80.38 NONCONC 12,173 83.39 93.32 2.1 Bag-of-words features MAC 79 63.33 24.05 Our simplest feature set, HASWORD-(tag-)word, PLACE 754 64.89 63.00 simply captures each word in the NP, both with and ORG 1,208 58.26 27.73 without its accompanying part-of-speech (POS) tag. MIX 29 7.14 3.45 CONCRETE 1402 58.82 37.58 2.2 Internal syntactic features ANIMAL 137 69.44 18.25 Motivated by the observation that syntactic heads HUMAN 11,320 91.19 93.30 tend to determine animacy class, we introduce two Overall 28,737 Accuracy: 84.90 features: HEAD-tag-word contains the head word of The next table shows the performance of each the phrase (extracted automatically from the parse) feature bundle when it alone is used in classification, 2We inadvertently did some initial feature selection using as well as the performance of the model when each training data that included both our training and test sets. While we have re-run all of those experiments, this introduces a possi- 3See Levin and Rappaport Hovav (2005) for a survey of ar- ble bias towards features which perform well on our test set. gument selection criteria, including animacy. feature bundle is excluded. We offer for comparison consider accuracy measured only over those hypoth- a baseline model that always chooses the most esized NPs which encompass the same string of frequent class, NONCONC. words as an NP in the gold standard data. Our parser generated correct (evaluable) NPs with preci- Only these features: Accuracy (%) sion 88.63% and recall 73.51%, but for these evalu- Bag of words 83.04 able NPs, accuracy was marginally better than on Internal Syntactic 75.85 hand-parsed data: 85.43% using all features. The External Syntactic 50.35 parser likely tended to misparse those NPs which All but these features: — were hardest for our model to classify. Bag of words 77.02 Internal syntactic 83.36 3.3 Error analysis External syntactic 84.58 A number of the errors made by the model pre- Most frequent class 42.36 sented above stem from ambiguous cases where Full model 84.90 head words, often pronouns, can take on referents of multiple animacy classes, and where there is no clear 3.1 Binary classification evidence within the bounds of the sentence of which one is correct. In the following example the model We test our model’s performance on the incorrectly assigns mine the class CONCRETE, and somewhat better-known task of binary nothing in the sentence provides evidence for the (ANIMATE/INANIMATE) classification by merging surprising correct class, HUMAN. the model’s class assignments into two sets after classification, following the grouping defined in Za- Well, I’ve used mine on concrete treated enen et al.4 While none of our architectural choices wood. were made with binary classification in mind, it is heartening to know that the model performs well on For a model to correctly treat cases like this, it would this easier task. be necessary to draw on a simple co-reference reso- Overall accuracy is 93.50%, while a baseline lution system and incorporate features dependent on plausibly co-referent sentences elsewhere in the text. model that labels each NP ANIMATE achieves only 53.79%. All of the feature sets contribute mea- The distinction between an organization (ORG) surably to the binary model, and external syntactic and a non-organized group of people (HUMAN) in features do much better on this task than on fine- this corpus is troublesome for our model.
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